url: =Poster Sessions – MICCAI 2019\nPaper – Poster ID assignment can be found here.\nSession 1: Microscopy and Photo-optical Imaging\nMonday, October 14, 12:00 – 13:00\nM-1-B-005 Synapse-Aware Skeleton Generation for Neural Circuits\nBrian Matejek, Donglai Wei, Xueying Wang, Jinglin Zhao, Kalman Palagyi, Hanspeter Pfister\nM-1-B-014 High-Resolution Diabetic Retinopathy Image Synthesis Manipulated by Grading and Lesions\nYi Zhou, Xiaodong He, Shanshan Cui, Fan Zhu, Li Liu, Ling Shao\nM-1-B-023 CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation\nLei Mou, Yitian Zhao, Li Chen, Jun Cheng, Zaiwang Gu, Huaying Hao, Hong Qi, Yalin Zheng, Alejandro Frangi, Jiang Liu\nM-1-B-032 DeepACE: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks\nLi Xiao, Chunlong Luo, Yufan Luo, Tianqi Yu, Chan Tian, Qiao Jie, Yi Zhao\nM-1-B-041 From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification\nYifan Zhang, Hanbo Chen, Ying Wei, Peilin Zhao, Jiezhang Cao, Mingkui Tan, Qingyao Wu, Xinjuan Fan, Xiaoying Lou, Hailing Liu, Jinlong Hou, Xiao Han, Jianhua Yao, Junzhou Huang\nM-1-B-048 Ki-GAN: Knowledge Infusion Generative Adversarial Network for Photoacoustic Image Reconstruction in vivo\nHengrong Lan, Kang Zhou, Changchun Yang, Jun Cheng, Jiang Liu, Shenghua Gao, Fei Gao\nM-1-B-057 A Deep Reinforcement Learning Framework for Frame-by-frame Plaque Tracking on Intravascular Optical Coherence Tomography Image\nGongning Luo, Suyu Dong, Kuanquan Wang, Dong Zhang, Yue Gao, Xin Chen, Henggui Zhang, Shuo Li\nM-1-B-071 Multi-Index Optic Disc Quantification via MultiTask Ensemble Learning\nRongchang Zhao, Zailiang Chen, Xiyao Liu, Beiji Zou, Shuo Li\nM-1-B-080 PM-NET: Pyramid Multi-Label Network for Optic Disc and Cup Segmentation\nPengshuai Yin, Qingyao Wu, Yanwu Xu, Huaqing Min, Ming Yang, Yubing Zhang, Mingkui Tan\nM-1-B-090 IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation\nYanning Zhou, Hao Chen, Jiaqi Xu, Qi Dou, Pheng-Ann Heng\nM-1-B-099 Patch Transformer for Multi-tagging Whole Slide Histopathology Images\nWeijian Li, Viet-Duy Nguyen, Haofu Liao, Matt Wilder, Ke Cheng, Jiebo Luo\nM-1-B-109 Enhanced Cycle-Consistent Generative Adversarial Network for Color Normalization of H&E Stained Images\nNiyun Zhou, De Cai, Xiao Han, Jianhua Yao\nM-1-B-114 Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer\nAmal Lahiani, Nassir Navab, Shadi Albarqouni, Eldad Klaiman\nM-1-B-123 Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography\nRhona Asgari, Jose Orlando, Sebastian Waldstein, Ferdinand Schlanitz, Magdalena Baratsits, Ursula Schmidt-Erfurth, Hrvoje Bogunovic\nM-1-B-132 Rectified Cross-Entropy and Upper Transition Loss for Weakly Supervised Whole Slide Image Classifier\nHanbo Chen, Xiao Han, Xinjuan Fan, Xiaoying Lou, Hailing Liu, Junzhou Huang, Jianhua Yao\nM-1-B-135 Nuclei Segmentation in Histopathological Images using Two-Stage Learning\nQingbo Kang, Qicheng Lao, Thomas Fevens\nM-1-B-158 Unifying Structure Analysis and Surrogate-driven Function Regression for Glaucoma OCT Image Screening\nXi Wang, Hao Chen, Luyang Luo, An-Ran Ran, Poemen Pui Man Chan, Clement Chee Yung Tham, Carol Yim Lui Cheung, Pheng-Ann Heng\nM-1-M-163 CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation\nPeng Liu, Bin Kong, Li Zhongyu, Shaoting Zhang, Ruogu Fang\nM-1-M-179 Pancreatic Cancer Detection in Whole Slide Images Using Noisy Label Annotations\nHan Le, Dimitris Samaras, Tahsin Kurc, Rajarsi Gupta, Shroyer Kenneth, Joel Saltz\nM-1-M-188 Uncertainty guided semisupervised segmentation of retinal layers in OCT images\nSuman Sedai, Bhavna Antony, Ravneet Rai, Katie Jones, Hiroshi Ishikawa, Joel Schuman, Wollstein Gadi, Rahil Garnavi\nM-1-M-193 Fully Deep Learning for Slit-lamp Photo based Nuclear Cataract Grading\nChaoxi Xu, Xiangjia Zhu, Wenwen He, Yi Lu, Xixi He, Zongjiang Shang, Jun Wu, Keke Zhang, Yinglei Zhang, Xianfang Rong, Zhennan Zhao, Lei Cai, Dayong Ding, Xirong Li\nM-1-M-202 PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis\nZixu Zhao, Huangjing Lin, Hao Chen, Pheng-Ann Heng\nM-1-M-209 Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification\nM-1-M-218 Deep Segmentation-Emendation Model for Gland Instance Segmentation\nYutong Xie, Hao Lu, Jianpeng Zhang, Chunhua Shen, Yong Xia\nM-1-M-223 Attention Guided Network for Retinal Image Segmentation\nM-1-M-232 Deep Instance-Level Hard Negative Mining Model for Histopathology Images\nMeng Li, Lin Wu, Arnold Wiliem, Kun Zhao, Teng Zhang, Brian Lovell\nM-1-M-239 EDA-Net: Dense Aggregation of Deep and Shallow Information Achieves Quantitative Photoacoustic Blood Oxygenation Imaging Deep in Human Breast\nChangchun Yang, Fei Gao\nM-1-M-248 As easy as 1, 2… 4? Uncertainty in counting tasks for medical imaging\nZach Eaton-Rosen, Thomas Varsavsky, Sebastien Ourselin, Jorge Cardoso\nM-1-M-253 Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis\nLuyang Luo, Hao Chen, Xi Wang, Qi Dou, Huangjing Lin, Juan Zhou, Gongjie Li, Pheng-Ann Heng\nM-1-M-262 Progressive Learning for Neuronal Population Reconstruction from Optical Microscopy Images\nJie Zhao, Xuejin Chen, Zhiwei Xiong, Dong Liu, Junjie Zeng, Yueyi Zhang, Zheng-Jun Zha, Guoqiang Bi, Feng Wu\nM-1-M-269 Encoding histopathological WSIs using GNN for scalable diagnostically relevant regions retrieval\nYushan Zheng, Bonan Jiang, Jun Shi, Haopeng Zhang, Fengying Xie\nM-1-M-278 Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response\nKazuya Nishimura, Ker Dai Fei Elmer, Ryoma Bise\nM-1-M-283 Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells\nArtem Lukoyanov, Isabella Haberbosch, Constantin Pape, Alwin Kraemer, Yannick Schwab, Anna Kreshuk\nM-1-M-292 GAN-Based Image Enrichment in Digital Pathology Boosts Segmentation Accuracy\nLaxmi Gupta, Barbara Klinkhammer, Peter Boor, Dorit Merhof, Michael Gadermayr\nM-1-M-299 Evidence Localization for Pathology Images using Weakly Supervised Learning\nYongxiang Huang, Albert Chung\nM-1-M-308 Active Appearance Model Induced Generative Adversarial Networks for Controlled Data Augmentation\nJianfei Liu, Christine Shen, Tao Liu, Nancy Aguilera, Johnny Tam\nM-1-M-313 Improving Nuclei/Gland Instance Segmentation in Histopathology Images by Full Resolution Neural Network and Spatial Constrained Loss\nHui Qu, Zhennan Yan, Gregory Riedlinger, Subhajyoti De, Dimitris Metaxas\nM-1-M-322 Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes\nJingru Yi, Pengxiang Wu, Hui Qu, Qiaoying Huang, Daniel J. Hoeppner, Dimitris Metaxas, Bo Liu\nM-1-M-332 Robust Non-negative Tensor Factorization, Diffeomorphic Motion Correction, and Functional Statistics to Understand Fixation in Fluorescence Microscopy\nNeel Dey, Jeffrey Messinger, Roland Theodore Smith, Christine Curcio, Guido Gerig\nM-1-F-347 Vessel-Net: Retinal Vessel Segmentation under Multi-path Supervision\nYicheng Wu, Yong Xia, Yang Song, Donghao Zhang, Dongnan Liu, Chaoyi Zhang, Weidong Cai\nM-1-F-356 Limited-Angle Diffuse Optical Tomography Image Reconstruction using Deep Learning\nHanene Ben Yedder, Majid Shokoufi, Ben Cardoen, Farid Golnaraghi, Ghassan Hamarneh\nM-1-F-374 CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels\nSam Maksoud, Arnold Wiliem, Kun Zhao, Teng Zhang, Lin Wu, Brian Lovell\nM-1-F-380 Deep Local-Global Refinement Network for Stent Analysis in IVOCT Images\nYuyu Guo, Lei Bi, Ashnil Kumar, Yue Gao, Ruiyan Zhang, Dagan Feng, Qian Wang, Jinman Kim\nM-1-F-394 Precise Separation of Adjacent Nuclei using a Siamese Neural Network\nMiguel Luna, Kwon Mungi, Sanghyun Park\nM-1-F-403 Biological Age Estimated from Retinal Imaging: A Novel Biomarker of Aging\nChi Liu, Wei Wang, Zhixi Li, Yu Jiang, Xiaotong Han, Jason Ha, Wei Meng, Mingguang He\nM-1-F-408 Unsupervised Segmentation of Micro-CT Images of Lung Cancer Specimen Using Deep Generative Models\nTakayasu Moriya, Hirohisa Oda, Midori Mitarai, Shota Nakamura, Holger Roth, Masahiro Oda, Kensaku Mori\nM-1-F-422 Multi-task learning of a deep K-nearest neighbour network for histopathological image classification and retrieval\nM-1-E-451 Cell Tracking with Deep Learning for Cell Detection and Motion Estimation in Low-Frame-Rate\nJunya Hayashida, Ryoma Bise\nM-1-E-456 Deep Multi-Instance Learning for survival prediction from Whole Slide Images\nJiawen Yao, Xinliang Zhu, Junzhou Huang\nM-1-E-461 Combined Learning for Similar Tasks with Domain-Switching Networks\nDaniel Bug, Dennis Eschweiler, Qianyu Liu, Justus Schock, Leon Weninger, Friedrich Feuerhake, Julia Schüler, Johannes Stegmaier, Dorit Merhof\nM-1-E-467 PlacentaNet: Automatic Morphological Characterization of Placenta Photos with Deep Learning\nYukun Chen, Chenyan Wu, Zhuomin Zhang, Jeffery Goldstein, Alison Gernand, James Wang\nM-1-E-476 ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images\nYeman Brhane Hagos, Priya Narayanan, Ayse U. Akarca, Teresa Maraﬁoti, Yinyin Yuan\nM-1-E-485 Unsupervised Subtyping of Cholangiocarcinoma Using A Deep Clustering Convolutional Autoencoder\nHassan Muhammad, Carlie S. Sigel, Gabriele Campanella, Thomas Boerner, Linda M. Pak, Stefan Büttner, Jan N.M. Ijzermans, Bas Groot Koerkamp, Michael Doukas, William Jarnagin, Amber Simpson, Thomas Fuchs\nM-1-E-496 Multiclass deep active learning for detecting red blood cell subtypes in brightfield microscopy images\nArio Sadafi, Niklas Koehler, Asya Makhro, Anna Bogdanova, Nassir Navab, Carsten Marr, Tingying Peng\nM-1-E-505 Whole-Sample Mapping of Cancerous and Benign Tissue Properties\nLydia Neary-Zajiczek, Clara Essmann, Neil Clancy, Aiman Haider, Elena Miranda, Amir Gander, Mike Shaw, Brian Davidson, Delmiro Fernandez Reyes, Vijay Pawar, Danail Stoyanov\nM-1-E-514 Accelerated ML-assisted Tumor Detection in High-Resolution Histopathology Images\nNikolas Ioannou, Milos Stanisavljevic, Andreea Anghel, Nikolaos Papandreou, Sonali Andani, Jan Hendrik Rüschoff, Peter Wild, Maria Gabrani, Charalampos Pozidis\nM-1-E-516 Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework\nNavid Alemi Koohbanani, Mostafa Jahanifar, Ali Gooya, Nasir Rajpoot\nM-1-E-521 Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification\nYuan Xue, Qianying Zhou, Jiarong Ye, L.Rodney Long, Sameer Antani, Carl Cornwell, Zhiyun Xue, Xiaolei Huang\nM-1-E-526 Coidentification of group-level hole structures in brain networks via Hodge Laplacian\nHyekyoung Lee, Moo Chung, Hyejin Kang, Hongyoon Choi, Youngmin Huh, Seunggyun Ha, Eunkyung Kim, Dong Soo Lee\nM-1-E-531 Diverse Multiple Prediction on Neural Image Reconstruction\nZe Ye, Cong Chen, Changhe Yuan, Chao Chen\nM-1-E-536 Local and Global Consistency Regularized Mean Teacher for Semi-supervised Nuclei Classification\nHai Su, Xiaoshuang Shi, Jinzheng Cai, Lin Yang\nMonday, October 14, 13:00 – 14:00\nM-2-B-001 Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE\nVishwesh Nath, Ilwoo Lyu, Kurt Schilling, Prasanna Parvathaneni, Colin Hansen, Yucheng Tang, Justin Blaber, Yuankai Huo, Vaibhav Janve, Yurui Gao, Iwona Stepniewska, Adam Anderson, Bennett Landman\nM-2-B-010 Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale\nArchit Rathore, Sourabh Palande, Jeffrey Anderson, Brandon Zielinski, Tom Fletcher, Bei Wang\nM-2-B-019 Predicting Early Stages of Neurodegenerative Diseases via Multi-task Low-rank Feature Learning\nYujia Zhao, Haijun Lei, Ee-Leng Tan, Jong Yih Kuo, Hancong Li, Baiying Lei\nM-2-B-028 A Dirty Multi-task Learning Method for Multi-modal Brain Imaging Genetics\nLei Du, Fang Liu, Kefei Liu, Xiaohui Yao, Shannon Risacher, Junwei Han, Lei Guo, Andrew Saykin, Li Shen\nM-2-B-037 Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation\nBaha Eddine Ezzine, Islem Rekik\nM-2-B-044 Inter-modality Dependence Induced Data Recovery for MCI Conversion Prediction\nTao Zhou, Kim Han Thung, Yu Zhang, Huazhu Fu, Jianbing Shen, Dinggang Shen, Ling Shao\nM-2-B-053 Combining Multiple Behavioral Measures and Multiple Connectomes via Multiway Canonical Correlation Analysis\nSiyuan Gao, Xilin Shen, Todd Constable, Dustin Scheinost\nM-2-B-062 Revealing Functional Connectivity by Learning Graph Laplacian\nMinjeong Kim, Amr Moussa, Peipeng Liang, Daniel Kaufer, Paul Laurienti, Guorong Wu\nM-2-B-067 Constructing Multi-Scale Connectome Atlas by Learning Common Topology of Brain Networks\nMinjeong Kim, Xiaofeng Zhu, Ziwen Peng, Peipeng Liang, Daniel Kaufer, Paul Laurienti, Guorong Wu\nM-2-B-076 Super-Resolved q-Space Deep Learning\nChuyang Ye, Yu Qin, Chenghao Liu, Yuxing Li, Xiangzhu Zeng, Zhiwen Liu\nM-2-B-084 Unsupervised deep learning for Bayesian brain MRI segmentation\nAdrian Dalca, Evan Yu, Polina Golland, Bruce Fischl, Mert Sabuncu, Juan Iglesias\nM-2-B-094 Hydranet: Data Augmentation for Regression Neural Networks\nFlorian Dubost, Gerda Bortsova, Hieab Adams, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen De Bruijne\nM-2-B-103 Symmetric Dual Adversarial Connectomic Domain Alignment for Predicting Isomorphic Brain Graph From a Baseline Graph\nM-2-B-110 Surface-based Tracking of U-fibers in the Superficial White Matter\nM-2-B-119 Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference\nChristian Wachinger, Benjamin Gutierrez Becker, Anna Rieckmann, Sebastian Pölsterl\nM-2-B-128 Clustering of longitudinal shape data sets using mixture of separate or branching trajectories\nM-2-B-139 Neural parameters estimation for brain tumor growth modeling\nM-2-B-147 Preprocessing, Prediction and Significance: Framework and Application to Brain Imaging\nMartin Nørgaard, Brice Ozenne, Claus Svarer, Vibe Frokjaer, Martin Schain, Stephen Strother, Melanie Ganz\nM-2-B-153 Let’s agree to disagree: learning highly debatable multirater labelling.\nCarole Sudre, Beatriz Gomez Anson, Silvia Ingala, Daniel Jimenez, Chris Lane, Lukas Haider, Thomas Varsavsky, Ryutaro Tanno, Lorna Smith, Sebastien Ourselin, Rolf Jäger, Jorge Cardoso\nM-2-B-162 Early Prediction of Alzheimer’s Disease progression using Variational Autoencoder\nSumana Basu, Konrad Wagstyl, Azar Zandifar, Louis Collins, Adriana Romero, Doina Precup\nM-2-M-167 Deep Multi-modal Latent Representation Learning for Automated Dementia Diagnosis\nTao Zhou, Mingxia Liu, Huazhu Fu, Jun Wang, Jianbing Shen, Ling Shao, Dinggang Shen\nM-2-M-172 Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network\nJiashuang Huang, Luping Zhou, Daoqiang Zhang, Lei Wang\nM-2-M-177 Multimodal Data Registration for Brain Structural Association Networks\nDavid Lee, Ashish Sahib, Ben Wade, Katherine Narr, Gerhard Hellemann, Roger Woods, Shantanu Joshi\nM-2-M-183 Consistent Brain Ageing Synthesis\nTian Xia, Agisilaos Chartsias, Sotirios Tsaftaris, The Alzheimers Disease Neuroimaging Initiative N/A\nM-2-M-192 Hierarchical Multi-Geodesic Model for Longitudinal Analysis of Temporal Trajectories of Anatomical Shape and Covariates\nSungmin Hong, James Fishbaugh, Jason Wolff, Martin Styner, Guido Gerig\nM-2-M-198 A Novel Graph Wavelet Model for Brain Multi-Scale Activational-connectional Feature Fusion\nWenyan Xu, Qing Li, Zhiyuan Zhu, Xia Wu\nM-2-M-207 Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data\nNiharika D’Souza, Mary Beth Nebel, Nicholas Wymbs, Stewart Mostofsky, Archana Venkataraman\nM-2-M-213 Constructing Consistent Longitudinal Brain Networks by Group-wise Graph Learning\nMd Turja, Leo Zsembik, Guorong Wu, Martin Styner\nM-2-M-222 DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography\nItay Benou, Tammy Riklin Raviv\nM-2-M-228 Variational Autoencoder for Regression: Application to Brain Aging Analysis\nQingyu Zhao, Nicolas Honnorat, Ehsan Adeli, Tuo Leng, Kilian Pohl\nM-2-M-237 Interpretable Feature Learning Using Multi-Output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis\nJun Wang, Ying Zhang, Tao Zhou, Zhaohong Deng, Huifang Huang, Wang Shitong\nM-2-M-243 AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation\nPierrick Coupé, Boris Mansencal, Michaël Clément, Rémi Giraud, Baudouin Denis De Senneville, Vinh-Thong Ta, Vincent Lepetit, Jose Manjon\nM-2-M-252 TopAwaRe: Topology-Aware Registration\nRune Nielsen, Sune Darkner, Aasa Feragen\nM-2-M-258 RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting\nElisabeth Hoppe, Florian Thamm, Gregor Koerzdoerfer, Christopher Syben, Franziska Schirrmacher, Mathias Nittka, Josef Pfeuffer, Heiko Meyer, Andreas Maier\nM-2-M-267 CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading\nPu Huang, Dengwang Li, Zhicheng Jiao, Dongming Wei, Guoshi Li, Qian Wang, Han Zhang, Dinggang Shen\nM-2-M-273 Surface-Volume Consistent Construction of Longitudinal Atlases for the Early Developing Brains\nSahar Ahmad, Zhengwang Wu, Gang Li, Li Wang, Weili Lin, Pew-Thian Yap, Dinggang Shen\nM-2-M-282 Deep Probabilistic Modeling of Glioma Growth\nJens Petersen, Paul Jäger, Fabian Isensee, Simon Kohl, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Kickingereder, Klaus H. Maier-Hein\nM-2-M-288 An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms\nM-2-M-297 Graph Neural Network for Interpreting Task-fMRI Biomarkers\nXiaoxiao Li, Nicha Dvornek, Yuan Zhou, Juntang Zhuang, Pamela Ventola, James Duncan\nM-2-M-303 Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments Over Progressions\nM-2-M-312 Disease Knowledge Transfer across Neurodegenerative Diseases\nRazvan Marinescu, Marco Lorenzi, Stefano B. Blumberg, Alexandra Young, Pere Planell Morell, Neil Oxtoby, Arman Eshaghi, Keir Yong, Sebastian Crutch, Polina Golland, Daniel Alexander\nM-2-M-318 Automated Parcellation of the Cortex using Structural Connectome Harmonics\nHoyt Taylor, Zhengwang Wu, Ye Wu, Dinggang Shen, Han Zhang, Pew-Thian Yap\nM-2-M-327 Probing Brain Micro-Architecture by Orientation Distribution Invariant Identification of Diffusion Compartments\nKhoi Huynh, Tiantian Xu, Ye Wu, Geng Chen, Kim-Han Thung, Haiyong Wu, Weili Lin, Dinggang Shen, Pew-Thian Yap\nM-2-M-331 Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments\nKhoi Huynh, Tiantian Xu, Ye Wu, Kim-Han Thung, Geng Chen, Weili Lin, Dinggang Shen, Pew-Thian Yap\nM-2-M-336 Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators\nBernhard Egger, Markus Schirmer, Florian Dubost, Marco Nardin, Natalia Rost, Polina Golland\nM-2-M-342 Dynamic Routing Capsule Networks for Mild Cognitive Impairment Diagnosis\nZhicheng Jiao, Tae-Eui Kam, Ye Wu, Eybposh Mohammad Hossein, Pu Huang, Li-Ming Hsu, Han Zhang, Dinggang Shen\nM-2-F-343 M-2-F-418 Integrating Heterogeneous Brain Networks for Predicting Brain Disease Conditions\nYanfu Zhang, Liang Zhan, Weidong Cai, Paul Thompson, Heng Huang\nM-2-F-352 Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework\nSayan Ghosal, Qiang Chen, Aaron L. Goldman, William Ulrich, Karen F. Berman, Daniel R. Weinberger, Venkata S. Mattay, Archana Venkataraman\nM-2-F-361 Identify Hierarchical Structures from Task-based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net\nWei Zhang, Lin Zhao, Qing Li, Shijie Zhao, Qinglin Dong, Xi Jiang, Tuo Zhang, Tianming Liu\nM-2-F-363 Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-state fMRI\nGuoshi Li, Yujie Liu, Yanting Zheng, Ye Wu, Pew-Thian Yap, Shijun Qiu, Han Zhang, Dinggang Shen\nM-2-F-370 Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices\nHuahong Zhang, Alessandra Valcarcel, Rohit Bakshi, Renxin Chu, Francesca Bagnato, Russell Shinohara, Kilian Hett, Ipek Oguz\nM-2-F-376 Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression\nDaniele Ravì, Daniel Alexander, Neil Oxtoby\nM-2-F-385 Optimal experimental design for biophysical modelling in multidimensional diffusion MRI\nSantiago Coelho, Jose M. Pozo, Sune Jespersen, Alejandro Frangi\nM-2-F-390 Fast Polynomial Approximation to Heat Diffusion in Manifolds\nShih-Gu Huang, Ilwoo Lyu, Anqi Qiu, Moo Chung\nM-2-F-399 Uncertainty-informed detection of epileptogenic brain malformations using Bayesian neural networks\nRavnoor Gill, Benoit Caldairou, Neda Bernasconi, Andrea Bernasconi\nM-2-F-404 Intrinsic Patch-based Cortical Anatomical Parcellation using Graph Convolutional Neural Network on Surface Manifold\nZhengwang Wu, Fenqiang Zhao, Jing Xia, Li Wang, Gang Li, Dinggang Shen\nM-2-F-413 Hierarchical parcellation of the cerebellum\nShuo Han, Aaron Carass, Jerry Prince\nM-2-F-418 M-2-M-296 Interpretable Multimodality Embedding Of Cerebral Cortex Using Attention Graph Network For Identifying Bipolar Disorder\nHuzheng Yang, Xiaoxiao Li, Yifan Wu, Siyi Li, Su Lu, James Duncan, James Gee, Shi Gu\nM-2-F-427 Joint Identification of Network Hub Nodes by Multivariate Graph Inference\nDefu Yang, Chenggang Yan, Feiping Nie, Xiaofeng Zhu, Md Turja, Leo Charles Peek Zsembik, Martin Styner, Guorong Wu\nM-2-F-434 Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks\nGihyun Kwon, Chihye Han, Daeshik Kim\nM-2-E-437 Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life\nZhen Zhou, Han Zhang, Li-Ming Hsu, Weili Lin, Gang Pan, Dinggang Shen\nM-2-E-442 Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage\nVidya M S, Yogish Mallya, Arun Shastry, Vijayananda J\nM-2-E-447 Complete Fetal Head Compounding from Multi-View 3D Ultrasound\nRobert Wright, Nicolas Toussaint, Alberto Gomez, Veronika Zimmer, Jacqueline Matthew, Emily Skelton, Bishesh Khanal, Bernhard Kainz, Daniel Rueckert, Joseph Hajnal, Julia Schnabel\nM-2-E-452 Revealing Developmental Regionalization of Infant Cerebral Cortex Based on Multiple Cortical Properties\nFan Wang, Chunfeng Lian, Zhengwang Wu, Li Wang, Weili Lin, John Gilmore, Dinggang Shen, Gang Li\nM-2-E-457 A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI\nTae-Eui Kam, Xuyun Wen, Bing Jin, Zhicheng Jiao, Li-Ming Hsu, Zhen Zhou, Yujie Liu, Koji Yamashita, Sheng-Che Hung, Weili Lin, Han Zhang, Dinggang Shen\nM-2-E-462 Harmonization of Infant Cortical Thickness using Surface-to-Surface Cycle-Consistent Adversarial Networks\nFenqiang Zhao, Zhengwang Wu, Li Wang, Weili Lin, Shunren Xia, Dinggang Shen, Gang Li\nM-2-E-471 Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network\nKimberlin van Wijnen, Florian Dubost, Pinar Yilmaz, M. Arfan Ikram, Wiro Niessen, Hieab Adams, Meike Vernooij, Marleen De Bruijne\nM-2-E-480 Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions\nFan Zhang, Nico Hoffmann, Suheyla Cetin Karayumak, Yogesh Rathi, Alexandra Golby, Lauren O’Donnell\nM-2-E-492 Early Development of Infant Brain Complex Network\nWeixiong Jiang, Han Zhang, Li-Ming Hsu, Dan Hu, Guoshi Li, Ye Wu, Dinggang Shen\nM-2-E-501 Fast and Scalable Optimal Transport for Brain Tractograms\nJean Feydy, Pierre Roussillon, Alain Trouve, Pietro Gori\nM-2-E-510 A matched filter decomposition of fMRI into resting and task components\nAnand Joshi, Haleh Akrami, Jian Li, Richard Leahy\nM-2-E-515 Cortical Surface Parcellation using Spherical Convolutional Neural Networks\nPrasanna Parvathaneni, Shunxing Bao, Vishwesh Nath, Neil Woodward, Daniel Classen, Carissa Cascio, David Zald, Yuankai Huo, Bennett Landman, Ilwoo Lyu\nM-2-E-520 Topographic Filtering of Tractograms as Vector Field Flows\nXinyu Nie, Yonggang Shi\nM-2-E-525 Learned attention maps for 3D CNN prediction of future disease progression from patient images\nAdrian Tousignant, Mahsa Dadar, Louis Collins, Doina Precup, Douglas Arnold, Tal Arbel\nM-2-E-530 Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression\nWonsik Jung, Ahmad Wisnu Mulyadi, Heung-Il Suk\nM-2-E-535 Surface-Based Spatial Pyramid Matching of Cortical Regions for Analysis of Cognitive Performance\nKris Campbell, Tom Fletcher, Jeffrey Anderson\nM-2-E-540 HR-CAM: Precise Localization of pathology using multi-level learning in CNNs\nSumeet Shinde, Tanay Chougule, Jitender Saini, Madhura Ingalhalikar\nSession 3: Segmentation and Registration 1\nMonday, October 14, 15:30 – 16:30\nM-3-B-002 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI\nChen Chen, Xiaopeng Liu, Meng Ding, Junfeng Zheng, Jiangyun Li\nM-3-B-011 Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network\nHulin Kuang, Bijoy Menon, Wu Qiu\nM-3-B-020 Discriminative Consistent Domain Generation for Semi-supervised Learning\nJun Chen, Guang Yang, Yanping Zhang, Shu Zhao, Raad Mohiaddin, Tom Wong, David Firmin, Heye Zhang, Jennifer Keegan\nM-3-B-029 Automatic Vertebrae Recognition from Arbitrary Spine MRI images by a Hierarchical Self-calibration Detection Framework\nShen Zhao, Xi Wu, Bo Chen, Shuo Li\nM-3-B-038 ET-Net: A Generic Edge-Attention Guidance Network for Medical Image Segmentation\nZhijie Zhang, Huazhu Fu, Hang Dai, Jianbing Shen, Yanwei Pang, Ling Shao\nM-3-B-045 Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation\nHaidong Zhu, Jialin Shi, Ji Wu\nM-3-B-054 Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention\nQingyi Tao, Zongyuan Ge, Jianfei Cai, Jianxiong (Terry) Yin, Simon See\nM-3-B-063 Multi-View Learning with Feature Level Fusion for Cervical Dysplasia Diagnosis\nTingting Chen, Xinjun Ma, Xuechen Liu, Wenzhe Wang, Ruiwei Feng, Jintai Chen, Chunnv Yuan, Weiguo Lu, Danny Chen, Jian Wu\nM-3-B-068 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation\nMagdalini Paschali, Stefano Gasperini, Abhijit Guha Roy, Michael Fang, Nassir Navab\nM-3-B-077 Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation\nLequan Yu, Shujun Wang, Xiaomeng Li, Chi-Wing Fu, Pheng-Ann Heng\nM-3-B-085 Radiomics-guided GAN for Segmentation of Liver Tumor without Contrast Agents\nXiaojiao Xiao, Juanjuan Zhao, Yan Qiang, Jaron Chong, Xiaotang Yang, Ntikurako Guy-Fernand Kazihise, Bo Chen, Shuo Li\nM-3-B-093 VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation\nM-3-B-102 Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss\nGuotai Wang, Jonathan Shapey, Wenqi Li, Reuben Dorent, Alex Demitriadis, Sotirios Bisdas, Ian Paddick, Robert Bradford, Sebastien Ourselin, Tom Vercauteren\nM-3-B-111 Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography\nM-3-B-120 A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes\nM-3-B-129 Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation\nFabian Balsiger, Yannick Soom, Olivier Scheidegger, Mauricio Reyes\nM-3-B-138 Multi-task Attention-based Semi-supervised Learning for Medical Image Segmentation\nM-3-B-146 Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning\nKai Wu, Bowen Du, Man Luo, Hongkai Wen, Yiran Shen, Jianfeng Feng\nM-3-B-152 Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation\nM-3-B-161 Integrating 3D Geometry of Organ for Improving Medical Imaging Segmentation\nM-3-M-166 LSRC: A Long-Short Range Context-Fusing Framework for Automatic 3D Vertebra Localization\nJintai Chen, Yanjie Wang, Ruoqian Guo, Bohan Yu, Tingting Chen, Wenzhe Wang, Ruiwei Feng, Danny Chen, Jian Wu\nM-3-M-171 Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation\nM-3-M-176 Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation\nChaowei Fang, Guanbin Li, Chengwei Pan, Yizhou Yu\nM-3-M-182 Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors\nM-3-M-191 From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network\nYuhang Liu, Zhen Zhou, Shu Zhang, Ling Luo, Qianyi Zhang, Fandong Zhang, Xiuli Li, Yizhou Wang, Yizhou Yu\nM-3-M-199 Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels\nMartin Zlocha, Qi Dou, Ben Glocker\nM-3-M-212 A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration\nRiddhish Bhalodia, Shireen Elhabian, Ladislav Kavan, Ross Whitaker\nM-3-M-221 Automated Pulmonary Embolism Detection from CTPA Images using an End-to-End Convolutional Neural Network\nYi Lin, Jianchao Su, Xiang Wang, Xiang Li, Jingen Liu, Kwang-Ting Cheng, Xin Yang\nM-3-M-229 Incompressible image registration using divergence-conforming B-splines\nLucas Fidon, Michael Ebner, Luis Garcia-Peraza Herrera, Marc Modat, Sebastien Ourselin, Tom Vercauteren\nM-3-M-242 Instance Segmentation from Volumetric Biomedical Images without Voxel-Wise Labeling\nMeng Dong, Dong Liu, Zhiwei Xiong, Xuejin Chen, Yueyi Zhang, Zheng-Jun Zha, Guoqiang Bi, Feng Wu\nM-3-M-251 Placental Flattening via Volumetric Parameterization\nMazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev, Ellen Grant, Justin Solomon, Polina Golland\nM-3-M-259 Simultaneous Lung Field Detection and Segmentation for Pediatric ChestRadiographs\nWei Zhang, Guanbin Li, Fuyu Wang, Longjiang E, Yizhou Yu, Liang Lin, Huiying Liang\nM-3-M-272 Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network\nRenzhen Wang, Shilei Cao, Kai Ma, Deyu Meng, Yefeng Zheng\nM-3-M-281 Dual Encoding U-Net for Retinal Vessel Segmentation\nWang Bo, Shuang Qiu, Huiguang He\nM-3-M-289 A deep learning approach to MR-less spatial normalization for tau PET images\nM-3-M-302 Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration\nLihao Liu, Xiaowei Hu, Lei Zhu, Pheng-Ann Heng\nM-3-M-311 Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images\nFei Yu, Jie Zhao, Yanjun Gong, Zhi Wang, Yuxi Li, Fan Yang, Bin Dong, Quanzheng Li, Li Zhang\nM-3-M-319 A Divide-and-Conquer Approach towards Understanding Deep Networks\nWeilin Fu, Andreas Maier, Nishant Ravikumar, Roman Schaffert, Katharina Breininger\nM-3-M-330 Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation\nJunlin Yang, Nicha Dvornek, Fan Zhang, Julius Chapiro, Ming De Lin, James Duncan\nM-3-M-335 Unsupervised Ensemble Strategy for Retinal Vessel Segmentation\nBo Liu, Lin Gu, Feng Lu\nM-3-M-341 Scalable Neural Architecture Search for 3D Medical Image Segmentation\nSungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup Kim\nM-3-F-344 Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification\nWenao Ma, Shuang Yu, Kai Ma, Jiexiang Wang, Xinghao Ding, Yefeng Zheng\nM-3-F-353 DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Deep Priori Anatomy\nYuting He, Guanyu Yang, Y Ang Chen, Youyong Kong, Jiasong Wu, Lijun Tang, Xiaomei Zhu, Jean-Louis Dillenseger, Huazhong Shu, Jean-Louis Coatrieux, Pengfei Shao, Shaobo Zhang, Shuo Li\nM-3-F-362 Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction\nWenjia Bai, Chen Chen, Giacomo Tarroni, Jinming Duan, Florian Guitton, Steffen Petersen, Yike Guo, Paul M. Matthews, Daniel Rueckert\nM-3-F-364 Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view images\nM-3-F-371 Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation\nM-3-F-377 Right Ventricle Segmentation in Short-Axis MRI Using A Shape Constrained Dense Connected U-net\nHao Yang, Zexiong Liu, Xuan Yang\nM-3-F-386 Semi-supervised Medical Image Segmentation via Learning Consistency under Transformations\nM-3-F-391 SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation\nKen C. L. Wong, Mehdi Moradi\nM-3-F-400 Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images\nWenguang Yuan, Jia Wei, Jiabing Wang, Qianli Ma, Tolga Tasdizen\nM-3-F-405 Improved Inference via Deep Input Transfer\nM-3-F-414 Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion\nDakai Jin, Dazhou Guo, Tsung-Ying Ho, Adam Harrison, Jing Xiao, Chen-Kan Tseng, Le Lu\nM-3-F-419 Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk\nM-3-F-428 Mixed-Supervised Dual-Network for Medical Image Segmentation\nDuo Wang, Ming Li, Nir Ben-Shlomo, C. Eduardo Corrales, Yu Cheng, Tao Zhang, Jayender Jagadeesan\nM-3-F-435 Context-Aware Inductive Bias Learning for Vessel Border Detection in Multi-modal Intracoronary Imaging\nZhifan Gao, Shuo Li\nM-3-E-438 Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints\nLong Chen, Martin Strauch, Dorit Merhof\nM-3-E-443 HFA-Net: 3D Cardiovascular Image Segmentation with Asymmetrical Pooling and Content-Aware Fusion\nHao Zheng, Lin Yang, Jun Han, Yizhe Zhang, Peixian Liang, Zhuo Zhao, Chaoli Wang, Danny Chen\nM-3-E-448 Fast and Accurate Electron Microscopy Image Registration with 3D Convolution\nShenglong Zhou, Zhiwei Xiong, Chang Chen, Xuejin Chen, Dong Liu, Yueyi Zhang, Zheng-Jun Zha, Feng Wu\nM-3-E-453 On the applicability of registration uncertainty\nJie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra Golby, Masashi Sugiyama, William Wells, Sarah Frisken\nM-3-E-458 Extreme Points Derived Confidence Map as a Cue for Class-Agnostic Interactive Segmentation Using Deep Neural Network\nShadab Khan, Ahmed Shahin, Javier Villafruela, Jianbing Shen, Ling Shao\nM-3-E-470 3DFPN-HS2: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection\nJingya Liu, Yingli Tian, Liangliang Cao, Oguz Akin\nM-3-E-479 DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation\nM-3-E-488 One Network To Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation\nMathias Perslev, Erik Dam, Akshay Pai, Christian Igel\nM-3-E-493 Echocardiography Segmentation by Quality Translation using Anatomically Constrained CycleGAN\nMohammad Jafari, Zhibin Liao, Hany Girgis, Mehran Pesteie, Robert Rohling, Ken Gin, Terasa Tsang, Purang Abolmaesumi\nM-3-E-502 PseudoEdgeNet: Nuclei Segmentation only with Point Annotations\nM-3-E-511 High Resolution Medical Image Segmentation using Data-swapping Method\nHaruki Imai, Samuel Matzek, Tung Le, Yasushi Negishi, Kiyokuni Kawachiya\nM-3-E-519 Statistical intensity- and shape-modeling to automate cerebrovascular segmentation from TOF-MRA data\nShoujun Zhou, Na Li, Aichi Chien, Zonghan Wu, Cheng Wang, Baochang Zhang, Tiexiang Wen\nM-3-E-524 MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection\nZihao Li, Shu Zhang, Junge Zhang, Kaiqi Huang, Yizhou Wang, Yizhou Yu\nM-3-E-529 X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies\nM-3-E-534 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation\nChao Huang, Hu Han, Qingsong Yao, Shankuan Zhu, S. Kevin Zhou\nM-3-E-539 AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks\nYulei Qin, Mingjian Chen, Hao Zheng, Yun Gu, Mali Shen, Jie Yang, Xiaolin Huang, Yuemin Zhu, Guang-Zhong Yang\nSession 4: Segmentation and Registration 2\nTuesday, October 15, 11:30 – 12:30\nT-4-B-003 Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation\nAlain Jungo, Mauricio Reyes\nT-4-B-012 Multi-View Semi-supervised 3D Whole Brain Segmentation with a Self-Ensemble Network\nYuan-Xing Zhao, Yanming Zhang, Ming Song, Cheng-Lin Liu\nT-4-B-021 Learning task-specific and shared representations in medical imaging\nT-4-B-030 The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN\nWenjun Yan, Qian Tao, Yuanyuan Wang\nT-4-B-039 LVC-Net: Medical image segmentation with noisy label based on Local Visual Cues\nYucheng Shu, Xiao Wu, Weisheng Li\nT-4-B-046 Selective Feature Aggregation Network with Area-boundary Constraints for Polyp Segmentation\nYuqi Fang, Cheng Chen, Delong Zhu, Yixuan Yuan, Kai-Yu Tong\nT-4-B-055 Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning\nSiyuan Pan, Xuhong Hou, Huating Li, Bin Sheng, Ruogu Fang, Yuxin Xue, Weiping Jia, Jing Qin\nT-4-B-064 Project & Excite’ Modules for Segmentation of Volumetric Medical Scans\nAnne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua, Christian Wachinger\nT-4-B-069 Multi-Class Gradient Harmonized Dice Loss with Application to Knee MR Image Segmentation\nQin Liu, Xiongfeng Tang, Deming Guo, Yanguo Qin, Pengfei Jia, Yiqiang Zhan, Xiang Zhou, Dijia Wu\nT-4-B-078 Extract Bone Parts without Human Prior: End-to-end Convolutional Neural Network for Pediatric Bone Age Assessment\nChuanbin Liu, Hongtao Xie, Yizhi Liu, Zheng-Jun Zha, Fanchao Lin, Yongdong Zhang\nT-4-B-086 Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation\nT-4-B-092 Mask-MCNet: Instance Segmentation in 3D Point Cloud of Intra-oral Scans\nFarhad Ghazvinian Zanjani, David Anssari Moin, Teo Cherici, Sarah Parinussa, Frank Claessen, Arash Pourtaherian, Sveta Zinger, P. H. N. De With\nT-4-B-101 Interactive deep editing framework for medical image segmentation\nBowei Zhou, Li Chen, Zhao Wang\nT-4-B-112 Weakly Supervised Segmentation Framework with Uncertainty: A Study on Pneumothorax Segmentation in Chest X-ray\nXi Ouyang, Zhong Xue, Yiqiang Zhan, Xiang Zhou, Qian Wang, Jie-Zhi Cheng\nT-4-B-121 Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice\nJeroen Bertels, Tom Eelbode, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew Blaschko\nT-4-B-125 Achieving Accurate Segmentation of Nasopharyngeal Carcinoma in MR Images through Recurrent Attention\nJia-Bin Huang, Enhong Zhuo, Haojiang Li, Lizhi Liu, Yangming Ou, Hongmin Cai\nT-4-B-130 CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke\nT-4-B-137 Matwo-CapsNet: a Multi-Label Semantic Segmentation Capsules Network\nT-4-B-145 Brain Segmentation from k-space with End-to-end Recurrent Attention Network\nQiaoying Huang, Xiao Chen, Dimitris Metaxas, Mariappan Nadar\nT-4-B-151 Integrating cross-modality hallucinated MR information with CT to aid mediastinal lung tumor segmentation\nJue Jiang, Yu-Chi Hu, Neelam Tyagi, Andreas Rimner, Berry Sean, Joseph Deasy, Harini Veeraraghavan\nT-4-B-160 PAN: Projective Adversarial Network for Medical Image Segmentation\nNaji Khosravan, Aliasghar Mortazi, Michael Wallace, Ulas Bagci\nT-4-M-165 Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks\nQi Zeng, Davood Karimi, Emily Ht Pang, Shahed Mohammed, Caitlin Schneider, Mohammad Honarvar, Septimiu Salcudean\nT-4-M-170 Image-and-Spatial Transformer Networks for Structure-Guided Image Registration\nMatthew Lee, Ozan Oktay, Andreas Schuh, Michiel Schaap, Ben Glocker\nT-4-M-175 Constrained Domain Adaptation for Segmentation\nT-4-M-181 Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images\nNicola Dinsdale, Mark Jenkinson, Ana Ineyda Namburete\nT-4-M-190 Automated detection and type classification of central venous catheters in chest X-rays\nVaishnavi Subramanian, Hongzhi Wang, Joy Wu, Ken C. L. Wong, Arjun Sharma, Tanveer Syeda-Mahmood\nT-4-M-200 Whole Heart and Great Vessel Segmentation in Congenital Heart Disease using Deep Neural Networks and Graph Matching\nXiaowei Xu, Yiyu Shi, Jian Zhuang, Haiyun Yuan, Meiping Huang, Tianchen Wang, Qianjun Jia\nT-4-M-211 Neural Architecture Search for Adversarial Medical Image Segmentation\nNanqing Dong, Min Xu, Xiaodan Liang, Yiliang Jiang, Wei Dai, Eric Xing\nT-4-M-220 Automatically Localizing a Large Set of Spatially Correlated Key Points: A Case Study in Spine Imaging\nAlexander Mader, Cristian Lorenz, Jens Von Berg, Carsten Meyer\nT-4-M-230 Cardiac MRI Segmentation with Strong Anatomical Guarantees\nNathan Painchaud, Youssef Skandrani, Thierry Judge, Olivier Bernard, Alain Lalande, Pierre-Marc Jodoin\nT-4-M-241 Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders\nAgostina Larrazabal, Cesar Martinez, Enzo Ferrante\nT-4-M-250 Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data\nChunwei Ma, Zhanghexuan Ji, Mingchen Gao\nT-4-M-260 Harnessing 2D Networks and 3D Features for Automated Pancreas Segmentation from Volumetric CT Images\nHuai Chen, Xiuying Wang, Yi-Jie Huang, Xiyi Wu, Yizhou Yu, Lisheng Wang\nT-4-M-271 Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images\nYizhe Zhang, Michael Ying, Danny Chen\nT-4-M-280 Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks\nNingning Zhao, Nuo Tong, Dan Ruan, Ke Sheng\nT-4-M-290 Group-wise Graph Matching of Cortical Gyral Hinges\nTuo Zhang, Xiao Li, Lin Zhao, Ying Huang, Zhibin He, Lei Guo, Tianming Liu\nT-4-M-301 CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion\nYuan Liang, Weinan Song, J.P. Dym, Kun Wang, Lei He\nT-4-M-310 Multi-view Graph Matching of Cortical Landmarks\nYing Huang, Zhibin He, Lei Guo, Tianming Liu, Tuo Zhang\nT-4-M-320 Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation\nXianxu Hou, Jingxin Liu, Bolei Xu, Bozhi Liu, Xin Chen, Mohammad Ilyas, Ian Ellis, Jon Garibaldi, Guoping Qiu\nT-4-M-329 HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images\nHaozhe Jia, Yang Song, Heng Huang, Weidong Cai, Yong Xia\nT-4-M-334 Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks\nT-4-M-340 Deep Cascaded Attention Networks for Multi-task Brain Tumor Segmentation\nHai Xu, Hongtao Xie, Yizhi Liu, Chuandong Cheng, Chaoshi Niu, Yongdong Zhang\nT-4-F-345 Probabilistic Atlases to Enforce Topological Constraints\nUdaranga Wickramasinghe, Graham Knott, Pascal Fua\nT-4-F-354 Dual-Stream Pyramid Registration Network\nMiao Kang, Xiaojun Hu, Weilin Huang, Matthew Scott, Mauricio Reyes\nT-4-F-365 ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths\nYanhao Zhu, Zhineng Chen, Shuai Zhao, Hongtao Xie, Wenming Guo, Yongdong Zhang\nT-4-F-372 Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy\nMohamed Elmahdy, Jelmer Wolterink, Hessam Sokooti, Ivana Isgum, Marius Staring\nT-4-F-378 Tubular Structure Segmentation Using Spatial Fully Connected Network With Radial Distance Loss for 3D Medical Images\nT-4-F-387 Fine-Scale Vessel Extraction in Fundus Images by Registration with Fluorescein Angiography\nKyoung Jin Noh, Sang Jun Park, Soochahn Lee\nT-4-F-392 A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation\nJiong Wu, Yue Zhang, Xiaoying Tang\nT-4-F-401 Online atlasing using an iterative centroid\nAntoine Legouhy, Olivier Commowick, Christian Barillot\nT-4-F-406 A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation\nRobin Brügger, Christian Baumgartner, Ender Konukoglu\nT-4-F-415 Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling\nT-4-F-420 Unsupervised Quality Control of Image Segmentation based on Bayesian Learning\nBenoît Audelan, Herve Delingette\nT-4-F-429 U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets\nThéo Estienne, Maria Vakalopoulou, Stergios Christodoulidis, Enzo Battistella, Marvin Lerousseau, Alexandre Carre, Guillaume Klausner, Roger Sun, Charlotte Robert, Stavroula Mougiakakou, Eric Deutsch, Nikos Paragios\nT-4-F-436 PHiSeg: Capturing Uncertainty in Medical Image Segmentation\nChristian Baumgartner, Kerem Can Tezcan, Ender Konukoglu, Krishna Chaitanya, Andreas Hoetker, Urs Muehlematter, Olivio Donati, Anton Becker, Khoschy Schawkat, Ender Konukoglu\nT-4-E-439 Generative adversarial network for segmentation of motion affected neonatal brain MRI\nNadieh Khalili, Elise Turk, Majd Zreik, Max Viergever, Manon Benders, Ivana Isgum\nT-4-E-444 Unsupervised Deformable Image Registration Using Cycle-Consistent CNN\nBoah Kim, Jieun Kim, June-Goo Lee, Dong Hwan Kim, Seong Ho Park, Jong Chul Ye\nT-4-E-449 mlVIRNET: Multilevel Variational Image Registration Network\nT-4-E-454 Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation\nDong Yang, Holger Roth, Ziyue Xu, Fausto Milletari, Ling Zhang, Daguang Xu\nT-4-E-459 Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images\nRula Amer, Jannette Nassar, David Bendahan, Noam Ben-Eliezer, Hayit Greenspan\nT-4-E-469 Curriculum semi-supervised segmentation\nHoel Kervadec, Jose Dolz, Ismail Ben Ayed, Eric Granger\nT-4-E-478 Bronchial Cartilage Assessment with Model-Based GAN Regressor\nPietro Nardelli, George R. Washko, Raul San Jose Estepar\nT-4-E-487 Fully convolutional boundary regression for retina OCT segmentation\nYufan He, Aaron Carass, Yihao Liu, Bruno Jedynak, Sharon Solomon, Shiv Saidha, Peter Calabresi, Jerry Prince\nT-4-E-494 Targeting Precision with Data Augmented Samples in Deep Learning\nPietro Nardelli, Raul San Jose Estepar\nT-4-E-503 Permutohedral Attention Module for Efficient Non-Local Neural Networks\nSamuel Joutard, Reuben Dorent, Amanda Isaac, Sebastien Ourselin, Tom Vercauteren, Marc Modat\nT-4-E-512 Spectral CT based training dataset generation and augmentation for conventional CT vascular segmentation\nPierre-Jean Lartaud, Aymeric Rouchaud, Jean-Michel Rouet, Olivier Nempont, Loïc Boussel\nT-4-E-518 A Soft STAPLE Algorithm Combined with Anatomical Knowledge\nEytan Kats, Jacob Goldberger, Hayit Greenspan\nT-4-E-523 Volumetric Attention for 3D Medical Image Segmentation and Detection\nXudong Wang, Shizhong Han, Yunqiang Chen, Dashan Gao, Nuno Vasconcelos\nT-4-E-528 Segmentation of Vessels in Ultra High Frequency Ultrasound Sequences using Contextual Memory\nTejas Sudharshan Mathai, Vijay Gorantla, John Galeotti\nT-4-E-533 Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation\nWoong Bae, Seungho Lee, Yeha Lee, Beomhee Park, Minki Chung, Kyu-Hwan Jung\nT-4-E-538 Improving Multi-Atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation\nLong Xie, Jiancong Wang, Mengjin Dong, David Wolk, Paul Yushkevich\nSession 5: Reconstruction, Synthesis and Image Quality\nTuesday, October 15, 13:00 – 14:00\nT-5-B-004 ImHistNet: Learnable Image Histogram Based DNN with Application to Noninvasive Determination of Carcinoma Grades in CT Scans\nMohammad Arafat Hussain, Ghassan Hamarneh, Rafeef Abugharbieh\nT-5-B-013 Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis\nZongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee, Ruibin Feng, Nima Tajbakhsh, Michael Gotway, Jianming Liang\nT-5-B-022 Direct Quantification for Coronary Artery Stenosis Using Multiview Learning\nDong Zhang, Guang Yang, Shu Zhao, Yanping Zhang, Heye Zhang, Shuo Li\nT-5-B-031 R2-Net: Recurrent and Recursive Network for Sparse-view CT Artifacts Removal\nTiancheng Shen, Xia Li, Zhisheng Zhong, Jianlong Wu, Zhouchen Lin\nT-5-B-040 Exploiting Reliability-guided Aggregation for the Assessment of Curvilinear Structure Tortuosity\nPan Su, Yitian Zhao, Tianhua Chen, Jianyang Xie, Yifan Zhao, Hong Qi, Yalin Zheng, Jiang Liu\nT-5-B-047 SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis\nTianyang Zhang, Huazhu Fu, Yitian Zhao, Jun Cheng, Mengjie Guo, Zaiwang Gu, Bing Yang, Yuting Xiao, Shenghua Gao, Jiang Liu\nT-5-B-056 Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition\nT-5-B-065 Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment\nT-5-B-070 Data-driven Enhancement of Blurry Retinal Images via Generative Adversarial Networks\nHe Zhao, Bingyu Yang, Lvchen Cao, Huiqi Li\nT-5-B-079 Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces\nHuazhu Fu, Boyang Wang, Jianbing Shen, Shanshan Cui, Yanwu Xu, Jiang Liu, Ling Shao\nT-5-B-091 Deep Learning Based Framework for Direct Reconstruction of PET Images\nZhiyuan Liu, Huai Chen, Huafeng Liu\nT-5-B-100 Unsupervised Anomaly Localization using Variational Auto-Encoders\nDavid Zimmerer, Klaus H. Maier-Hein\nT-5-B-108 Consensus Neural Network for Medical Image Denoising with Only Noisy Training Samples\nDufan Wu, Kuang Gong, Kyungsang Kim, Xiang Li, Quanzheng Li\nT-5-B-113 Stereo-Correlation and Noise-Distribution Aware ResVoxGAN for Dense Slices Reconstruction and Noise Reduction in Thick Low-Dose CT\nRongjun Ge, Y Ang Chen, Limin Luo, Guanyu Yang, Shuo Li\nT-5-B-122 Detection and Correction of Cardiac MRI Motion Artefacts during Reconstruction from k-space\nIlkay Oksuz, James Clough, Bram Ruijsink, Esther Puyol Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Daniel Rueckert, Andrew King, Julia Schnabel\nT-5-B-131 Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction\nJo Schlemper, Sadegh Salehi, Prantik Kundu, Carole Lazarus, Hadrien Dyvorne, Daniel Rueckert, Michal Sofka\nT-5-B-136 Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis\nAnjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch, Maximilian Loeffler, Jan Kirschke, Bjoern Menze\nT-5-B-144 Wavelet-Based Semi-Supervised Adversarial Learning for Synthesizing Realistic 7T from 3T MRI\nLiangqiong Qu, Shuai Wang, Pew-Thian Yap, Dinggang Shen\nT-5-B-150 Concept-Centric Visual Turing Tests for Method Validation\nT-5-B-159 Bayesian Volumetric Autoregressive generative models for better semisupervised learning\nGuilherme Pombo, Robert Gray, Parashkev Nachev, John Ashburner, Thomas Varsavsky\nT-5-M-164 Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking\nYuhua Chen, Jaime Shaw, Yibin Xie, Debiao Li, Anthony Christodoulou\nT-5-M-169 Semi-Supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control\nMahmoud Mostapha, Juan Prieto, Veronica Murphy, Jessica Girault, Mark Foster, Joseph Blocher, Ashley Rumple, Weili Lin, Jed Ellison, John Gilmore, Steve Pizer, Martin Styner\nT-5-M-174 Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance with Application to Skin Lesion Classification\nChris Yoon, Ghassan Hamarneh, Rafeef Abugharbieh\nT-5-M-180 Enhancing OCT Signal by Fusion of GANs: Improving Statistical Power of Glaucoma Trials\nGeorgios Lazaridis, Marco Lorenzi, Sebastien Ourselin, David Garway-Heath\nT-5-M-189 Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging\nEvan Hann, Luca Biasiolli, Qiang Zhang, Iulia Popescu, Konrad Werys, Elena Lukaschuk, Valentina Carapella, Jose Paiva, Nay Aung, Jennifer Rayner, Kenneth Fung, Henrike Puchta, Mihir Sanghvi, Niall Moon, Katharine Thomas, Vanessa Ferreira, Steffen Petersen, Stefan Neubauer, Stefan Piechnik\nT-5-M-201 GANReDL: Medical Image enhancement using a generative adversarial network with real-order derivative induced loss functions\nPan Liu, Chao Li, Carola-Bibiane Schönlieb\nT-5-M-210 VS-Net: Variable spitting network for accelerated parallel MRI reconstruction\nJinming Duan, Jo Schlemper, Chen Qin, Cheng Ouyang, Wenjia Bai, Carlo Biffi, Ghalib Bello, Ben Statton, Declan O’Regan, Daniel Rueckert\nT-5-M-219 k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations\nChen Qin, Jo Schlemper, Jinming Duan, Gavin Seegoolam, Anthony Price, Joseph Hajnal, Daniel Rueckert\nT-5-M-231 3D Surface-Based Geometric and Topological Quantification of Retinal Microvasculature in OCT-Angiography via Reeb Analysis\nJiong Zhang, Amir Kashani, Yonggang Shi\nT-5-M-240 Exploiting motion for deep learning reconstruction of extremely-undersampled dynamic MRI\nGavin Seegoolam, Chen Qin, Jo Schlemper, Anthony Price, Joseph Hajnal, Daniel Rueckert\nT-5-M-249 Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans using Sparse Fidelity Loss and Adversarial Regularization\nKai Xuan, Dongming Wei, Yiqiang Zhan, Dijia Wu, Qian Wang\nT-5-M-261 Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images\nT-5-M-270 DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis\nHongwei Li, Johannes Paetzold, Anjany Sekuboyina, Florian Kofler, Jianguo Zhang, Jan Kirschke, Benedict Wiestler, Bjoern Menze\nT-5-M-279 Deep Gated Neural Network for QSM Background Field Removal\nJuan Liu, Kevin Koch\nT-5-M-291 A Two-Stage Multi-Loss Super-Resolution Network For Arterial Spin Labeling Magnetic Resonance Imaging\nZheng Li, Qingping Liu, Yiran Li, Qiu Ge, Yuanqi Shang, Donghui Song, Ze Wang, Jun Shi\nT-5-M-300 Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction\nHaofu Liao, Wei-An Lin, Jianbo Yuan, S. Kevin Zhou, Jiebo Luo\nT-5-M-309 Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction\nHaofu Liao, Wei-An Lin, Zhimin Huo, William Sehnert, Levon Vogelsang, S. Kevin Zhou, Jiebo Luo\nT-5-M-321 A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation\nRongzhao Zhang, Albert Chung\nT-5-M-328 BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization\nIl Yong Chun, Xuehang Zheng, Yong Long, Jeffrey Fessler\nT-5-M-333 Multi-Stage Image Quality Assessment of Diffusion MRI via Semi-Supervised Nonlocal Residual Networks\nSiyuan Liu, Kim Han Thung, Weili Lin, Pew-Thian Yap, Dinggang Shen\nT-5-M-339 Model Learning: Primal Dual Networks for Fast MR imaging\nT-5-F-346 A Prior Learning Network for Joint Image and Sensitivity Estimation in Parallel MR Imaging\nNan Meng, Yan Yang, Zongben Xu, Jian Sun\nT-5-F-355 Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks\nYoonmi Hong, Geng Chen, Pew-Thian Yap, Dinggang Shen\nT-5-F-366 Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging\nYanxia Chen, Taohui Xiao, Cheng Li, Qiegen Liu, Shanshan Wang\nT-5-F-373 Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation\nReuben Dorent, Samuel Joutard, Marc Modat, Sebastien Ourselin, Tom Vercauteren\nT-5-F-379 Isotropic MRI Super-Resolution Reconstruction with Multi-Scale Gradient Field Prior\nYao Sui, Onur Afacan, Ali Gholipour, Simon Warfield\nT-5-F-393 Echocardiography View Classification Using Quality Transfer Star Generative Adversarial Networks\nZhibin Liao, Mohammad Jafari, Hany Girgis, Robert Rohling, Ken Gin, Purang Abolmaesumi, Terasa Tsang\nT-5-F-402 Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets\nLe Zhang, Marco Pereanez, Christopher Bowles, Stefan Piechnik, Stefan Neubauer, Steffen Petersen, Alejandro Frangi\nT-5-F-407 SWTV-ACE: Spatially Weighted Regularization based Attenuation Coefficient Estimation Method for Hepatic Steatosis Detection\nFarah Deeba, Caitlin Schneider, Shahed Mohammed, Mohammad Honarvar, Edward Tam, Septimiu Salcudean, Robert Rohling\nT-5-F-416 Unsupervised Standard Plane Synthesis in Population Cine MRI via Cycle-Consistent Adversarial Networks\nT-5-F-421 Fused Detection of Retinal Biomarkers in OCT Volumes\nThomas Kurmann, Pablo Márquez Neila, Siqing Yu, Marion Munk, Sebastian Wolf, Raphael Sznitman\nT-5-F-430 RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting\nZhenghan Fang, Yong Chen, Dong Nie, Weili Lin, Dinggang Shen\nT-5-E-440 Learned Full-sampling Reconstruction\nWeilin Cheng, Yu Wang, Ying Chi, Xuansong Xie, Yuping Duan\nT-5-E-445 A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation using Deep Learning\nLuyao Shi, John Onofrey, Enette Mae Revilla, Takuya Toyonaga, David Menard, Joseph Ankrah, Richard Carson, Chi Liu, Yihuan Lu\nT-5-E-450 Single Image Based Reconstruction of High Field-like MR Images\nT-5-E-455 Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework\nViswanath Ps, Kratika Gupta, Gary Egan, Zhaolin Chen, Suyash Awate\nT-5-E-460 A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement\nUddeshya Upadhyay, Suyash Awate\nT-5-E-468 Image data validation for medical systems\nPablo Márquez Neila, Raphael Sznitman\nT-5-E-477 Multi-Stage Prediction Networks for Data Harmonization\nStefano B. Blumberg, Marco Palombo, Can Son Khoo, Chantal Tax, Ryutaro Tanno, Daniel Alexander\nT-5-E-486 3D Tiled Convolution for Effective Segmentation of Volumetric Medical Images\nT-5-E-495 Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment\nFlorin Ghesu, Bogdan Georgescu, Eli Gibson, Sebastian Guendel, Mannudeep Kalra, Ramandeep Singh, Subba Digumarthy, Sasa Grbic, Dorin Comaniciu\nT-5-E-504 Linear Time Invariant Model based Motion Correction (LiMo-Moco) of Dynamic Radial Contrast Enhanced MRI\nJaume Coll-Font, Onur Afacan, Jeanne Chow, Sila Kurugol\nT-5-E-513 Deep Variational Networks with Exponential Weighting for Learning Computed Tomography\nT-5-E-517 Cone-Beam Computed Tomography (CBCT) segmentation by adversarial learning domain adaptation\nXiaoqian Jia, Sicheng Wang, Xiao Liang, Anjali Balagopal, Dan Nguyen, Ming Yang, Zhangyang Wang, Xiaoning Qian, Jim Xiuquan Ji, Steve Jiang\nT-5-E-522 Medical-based Deep Curriculum Learning for Improved Fracture Classification\nAmelia Jimenez-Sanchez, Diana Mateus, Sonja Kirchhoff, Chlodwig Kirchhoff, Peter Biberthaler, Nassir Navab, Miguel Angel González Ballester, Gemma Piella\nT-5-E-527 Model-based reconstruction for highly accelerated first-pass perfusion cardiac MRI\nTeresa Correia, Torben Schneider, Amedeo Chiribiri\nT-5-E-532 Hybrid Generative Adversarial Networks for Deep MR to CT Synthesis using Unpaired Data\nT-5-E-537 Synthesize Mammogram from Digital Breast Tomosynthesis with Gradient Guided cGANs\nGongfa Jiang, Yao Lu, Jun Wei, Yuesheng Xu\nSession 6: Computer-Aided Diagnosis, Detection, and Classification 1\nTuesday, October 15, 15:30 – 16:30\nT-6-B-006 Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma\nZhuotun Zhu, Yingda Xia, Lingxi Xie, Elliot Fishman, Alan Yuille\nT-6-B-015 FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images\nYunhe Gao, Rui Huang, Hongsheng Li\nT-6-B-024 Multi-Resolution Path CNN with Deep Supervision for Intervertebral Disc Localization and Segmentation\nYunhe Gao, Chang Liu, Liang Zhao\nT-6-B-033 Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation\nMing Li, Weiwei Zhang, Guang Yang, Chengjia Wang, Heye Zhang, Huafeng Liu, Wei Zheng, Shuo Li\nT-6-B-042 Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning\nMohamed Akrout, Amir-Massoud Farahmand, Tory Jarmain, Latif Abid\nT-6-B-049 Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation\nYuyin Zhou, Yingwei Li, Zhishuai Zhang, Yan Wang, Angtian Wang, Elliot Fishman, Alan Yuille, Seyoun Park\nT-6-B-058 Bronchus Segmentation and Classification by Neural Networks and Linear Programming\nTianyi Zhao, Zhaozheng Yin, Jiao Wang, Dashan Gao, Yunqiang Chen, Yunxiang Mao\nT-6-B-072 Multi-label Thoracic Disease Image Classification with Cross-attention Networks\nCongbo Ma, Hu Wang, Steven Hoi\nT-6-B-081 Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting\nRunnan Chen, Yuexin Ma, Nenglun Chen, Daniel Lee, Wenping Wang\nT-6-B-089 MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation\nKe Yan, Youbao Tang, Yifan Peng, Veit Sanfort, Mohammadhadi Bagheri, Zhiyong Lu, Ronald Summers\nT-6-B-098 Intracranial aneurysms detection in 3D cerebrovascular mesh model with ensemble deep learning\nMingsong Zhou, Xingce Wang, Zhongke Wu, Jose M. Pozo, Alejandro Frangi\nT-6-B-107 Invertible Network for Classification and Biomarker Selection for ASD\nJuntang Zhuang, Nicha Dvornek, Xiaoxiao Li, Pamela Ventola, James Duncan\nT-6-B-115 Detecting Cannabis-Associated Cognitive Impairment using Resting-state fNIRS\nYingying Zhu, Jodi Gilman, Anne Evins, Mert Sabuncu\nT-6-B-124 Supervised Uncertainty Quantification for Segmentation with Multiple Annotations\nShi Hu, Daniel Worrall, Stefan Knegt, Bastiaan Veeling, Henkjan Huisman, Max Welling\nT-6-B-134 An Automated Cobb Angle Estimation Method Using Convolutional Neural Network with Area Limitation\nKailai Zhang, Nanfang Xu, Guosheng Yang, Xiangling Fu, Ji Wu\nT-6-B-143 Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion\nT-6-B-149 Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-Modal Neuroimages\nYongsheng Pan, Mingxia Liu, Chunfeng Lian, Yong Xia, Dinggang Shen\nT-6-B-157 Detecting Acute Strokes from Non-Contrast CT Scan Data Using Deep Convolutional Neural Networks\nDmitriy Umerenkov, Alexander Tuzhilin, Manvel Avetisian, Vladimir Kokh\nT-6-M-178 GraphXNET Chest X-Ray Classification Under Extreme Minimal Supervision\nAngelica Aviles-Rivero, Nicolas Papadakis, Ruoteng Li, Philip Sellars, Qingnan Fan, Robby Tan, Carola Schoenlieb\nT-6-M-187 NoduleNet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation\nT-6-M-194 Semi-supervised Segmentation of Liver using Adversarial Learning with Deep Atlas Prior\nHan Zheng, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Qingqing Chen, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen, Ruofeng Tong, Jian Wu\nT-6-M-203 Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization\nT-6-M-208 Multi-Instance Multi-Scale CNN for Medical Image Classification\nShaohua Li, Yong Liu, Xiuchao Sui, Cheng Chen, Gabriel Tjio, Daniel Ting, Rick Goh\nT-6-M-217 Leveraging Other Datasets for Medical Imaging Classification: Evaluation of Transfer, Multi-task and Semi-supervised Learning\nHong Shang, Zhongqian Sun, Wei Yang, Xinghui Fu, Han Zheng, Jia Chang, Junzhou Huang\nT-6-M-224 Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map\nMuhammad Febrian Rachmadi, María Del C. Valdés Hernández, Stephen Makin, Joanna Wardlaw, Taku Komura\nT-6-M-233 Comparative Evaluation of Hand-Engineered and Deep-Learned Features for Neonatal Hip Bone Segmentation in Ultrasound\nHoussam El-Hariri, Kishore Mulpuri, Antony J Hodgson, Rafeef Abugharbieh\nT-6-M-238 Mask Embedding for Realistic High-resolution Medical Image Synthesis\nYinhao Ren, Zhe Zhu, Yingzhou Li, Dehan Kong, Rui Hou, Lars Grimm, Maciej Mazurowski, Jeffrey Marks, Joseph Lo\nT-6-M-247 RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation\nHang Zhang, Jinwei Zhang, Qihao Zhang, Jeremy Kim, Shun Zhang, Susan A. Gauthier, Pascal Spincemaille, Thanh D. Nguyen, Mert Sabuncu, Yi Wang\nT-6-M-254 InfoMask: Masked Variational Latent Representation to Localize Chest Disease\nT-6-M-263 Unsupervised Clustering of Quantitative Imaging Subtypes using Autoencoder and Gaussian Mixture Model\nJianan Chen, Laurent Milot, Helen Cheung, Anne Martel\nT-6-M-268 End-to-End Dementia Status Prediction from Brain MRI using Multi-Task Weakly-Supervised Attention Network\nT-6-M-277 Robust and Discriminative Brain Genome Association Analysis\nXiaofeng Zhu, Dinggang Shen\nT-6-M-284 Graph convolution based attention model for personalized disease prediction\nAnees Kazi, Shayan Shekarforoush, Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Benedict Wiestler, Karsten Kortuem, Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab\nT-6-M-293 Arterial Spin Labeling Images Synthesis via Locally-constrained WGAN-GP Ensemble\nWei Huang, Mingyuan Luo, Xi Liu, Peng Zhang, Huijun Ding, Dong Ni\nT-6-M-298 Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification\nChristoph Haarburger, Michael Baumgartner, Daniel Truhn, Mirjam Broeckmann, Hannah Schneider, Simone Schrading, Christiane Kuhl, Dorit Merhof\nT-6-M-307 Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik’s Cube\nXinrui Zhuang, Yuexiang Li, Yifan Hu, Kai Ma, Yujiu Yang, Yefeng Zheng\nT-6-M-314 Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation\nChaowei Tan, Zhennan Yan, Shaoting Zhang, Kang Li, Dimitris Metaxas\nT-6-M-323 Feature Transformers: Privacy Preserving Life Learning Framework for Healthcare Applications\nHariharan Ravishankar, Pavan Annangi, Rahul Venkataramani, Saihareesh Anamandra, Prasad Sudhakar\nT-6-F-348 Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype\nZhenyu Tang, Yuyun Xu, Zhicheng Jiao, Junfeng Lu, Abudumijiti Aibaidula, Jinsong Wu, Qian Wang, Han Zhang, Dinggang Shen\nT-6-F-357 Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster\nQingbin Shao, Lijun Gong, Kai Ma, Hualuo Liu, Yefeng Zheng\nT-6-F-375 TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays\nYuxing Tang, Youbao Tang, Veit Sanfort, Jing Xiao, Ronald Summers\nT-6-F-381 Diagnosis-guided multi-modal feature selection for prognosis prediction of lung squamous cell carcinoma\nWei Shao, Tongxin Wang, Zhi Huang, Jun Cheng, Zhi Han, Daoqiang Zhang, Kun Huang\nT-6-F-395 Spatiotemporal Breast Mass Detection Network (MD-Net) in 4D DCE-MRI Images\nLixi Deng, Sheng Tang, Huazhu Fu, Bin Wang, Yongdong Zhang\nT-6-F-409 Longitudinal Change Detection on Chest X-rays using Geometric Correlation Maps\nDong Yul Oh, Jihang Kim, Kyong Joon Lee\nT-6-F-423 Improving Robustness of Medical Image Diagnosis with Denoising Convolutional Neural Networks\nFei-Fei Xue, Jin Peng, Ruixuan Wang, Qiong Zhang, Wei-Shi Zheng\nT-6-E-466 Novel Iterative Attention Focusing Strategy for Joint Pathology Localization and Diagnosis of MCI Progression\nQingfeng Li, Xiaodan Xing, Ying Sun, Bin Xiao, Hao Wei, Quan Huo, Minqing Zhang, Xiang Zhou, Yiqiang Zhan, Zhong Xue, Feng Shi\nT-6-E-475 Improving Uncertainty Estimation in Convolutional Neural Networks Using Inter-rater Agreement\nMartin Jensen, Dan Jørgensen, Raluca Jalaboi, Mads Hansen, Martin Aastrup Olsen\nT-6-E-484 Global and Local Interpretability for Cardiac MRI Classification\nJames Clough, Ilkay Oksuz, Esther Puyol Anton, Bram Ruijsink, Andrew King, Julia Schnabel\nT-6-E-497 3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata\nRahman Attar, Marco Pereanez, Christopher Bowles, Stefan Piechnik, Stefan Neubauer, Steffen Petersen, Alejandro Frangi\nT-6-E-506 Discriminative Coronary Artery Tracking via 3D CNN in Cardiac CT Angiography\nHan Yang, Junxuan Chen, Ying Chi, Xuansong Xie, Xiansheng Hua\nSession 7: Computer-Aided Diagnosis, Detection, and Classification 2\nWednesday, October 16, 11:30 – 12:30\nW-7-B-007 PRSNet: Part Relation and Selection Network for Bone Age Assessment\nW-7-B-016 Misshapen Pelvis Landmark Detection by Spatial Local Correlation Mining for Diagnosing Developmental Dysplasia of the Hip\nChuanbin Liu, Hongtao Xie, Sicheng Zhang, Jingyuan Xu, Jun Sun, Yongdong Zhang\nW-7-B-025 DScGANS: Integrate Domain Knowledge in Training Dual-Path Semi-Supervised Conditional Generative Adversarial Networks and S3VM for Ultrasonography Thyroid Nodules Classification\nWenkai Yang, Juanjuan Zhao, Yan Qiang, Xiaotang Yang, Yunyun Dong, Qianqian Du, Guohua Shi, Muhammad Bilal Zia\nW-7-B-034 Regression-based Line Detection Network for Delineation of Largely Deformed Brain Midline\nHao Wei, Xiangyu Tang, Minqing Zhang, Qingfeng Li, Xiaodan Xing, Xiang Zhou, Zhong Xue, Wenzhen Zhu, Zailiang Chen, Feng Shi\nW-7-B-043 Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis\nHeyi Li, Dongdong Chen, Bill Nailon, Mike Davies, David Laurenson\nW-7-B-050 Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction\nDan Hu, Han Zhang, Zhengwang Wu, Weili Lin, Gang Li, Dinggang Shen\nW-7-B-059 A CNN-Based Framework for Statistical Assessment of Spinal Shape and Curvature in Whole-Body MRI Images of Large Populations\nPhilipp Ernst, Georg Hille, Christian Hansen, Klaus Toennies, Marko Rak\nW-7-B-073 Automatic paraspinal muscle segmentation in patients with lumbar pathology using deep convolutional neural network\nWenyao Xia, Maryse Fortin, Joshua Ahn, Hassan Rivaz, Michele Battie, Terence M Peters, Yiming Xiao\nW-7-B-082 Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics\nFelix Denzinger, Michael Wels, Nishant Ravikumar, Katharina Breininger, Anika Reidelshöfer, Joachim Eckert, Michael Sühling, Axel Schmermund, Andreas Maier\nW-7-B-088 Task Adaptive Metric Space for Medium-Shot Medical Image Classification\nXiang Jiang, Liqiang Ding, Mohammad Havaei, Andrew Jesson, Stan Matwin\nW-7-B-097 DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning\nJen-Tang Lu, Rupert Brooks, Stefan Hahn, Jin Chen, Varun Buch, Gopal Kotecha, Katherine Andriole, Brian Ghoshhajra, Joel Pinto, Paul Vozila, Mark Michalski, Neil Tenenholtz\nW-7-B-106 Deep Multi Label Classification in Affine Subspaces\nThomas Kurmann, Pablo Márquez Neila, Sebastian Wolf, Raphael Sznitman\nW-7-B-116 Dynamic Spectral Convolution Networks with Assistant Task Training for Early MCI diagnosis\nXiaodan Xing, Qingfeng Li, Hao Wei, Minqing Zhang, Yiqiang Zhan, Xiang Zhou, Zhong Xue, Feng Shi\nW-7-B-126 LSTM Network for Prediction of Hemorrhagic Transformation in Acute Stroke\nYannan Yu, Bhargav Parsi, William Speier, Corey Arnold, Min Lou, Fabien Scalzo\nW-7-B-133 Similarity steered generative adversarial network and adaptive transfer learning for malignancy characterization of hepatocellualr carcinoma\nHanqiu Ju, Wanwei Jian, Xiaoping Cen, Guangyi Wang, Wu Zhou\nW-7-B-142 INN: Inflated Neural Networks for IPMN Diagnosis\nRodney Lalonde, Irene Tanner, Katerina Nikiforaki, Georgios Papadakis, Pujan Kandel, Candice Bolan, Michael Wallace, Ulas Bagci\nW-7-B-148 Contextual Deep Regression Network for Volume Estimation in Orbital CT\nShikha Chaganti, Camilo Bermudez, Louise Mawn, Thomas Lasko, Bennett Landman\nW-7-B-156 Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation\nUtku Ozbulak, Wesley De Neve, Arnout Van Messem\nW-7-M-173 Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images\nHejie Cui, Xinglong Liu, Ning Huang\nW-7-M-186 Efficient Soft-Constrained Clustering for Group-Based Labeling\nRyoma Bise, Kentaro Abe, Hideaki Hayashi, Kiyohito Tanaka, Seiichi Uchida\nW-7-M-195 Uncertainty measurements for the reliable classification of mammograms\nW-7-M-204 Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation\nYunyan Xing, Zongyuan Ge, Rui Zeng, Dwarikanath Mahapatra, Jarrel Seah, Meng Law, Tom Drummond\nW-7-M-216 Adversarial Policy Gradient for Deep Learning Image Augmentation\nVictor Cheng, Claudia Iriondo, Francesco Caliva, Justin Krogue, Sharmila Majumdar, Valentina Pedoia\nW-7-M-225 Decoding brain functional connectivity implicated in AD and MCI\nSukrit Gupta, Yi Hao Chan, Jagath Rajapakse\nW-7-M-234 Robust Cochlear Modiolar Axis Detection in CT\nWilhelm Wimmer, Clair Vandersteen, Nicolas Guevara, Marco Caversaccio, Herve Delingette\nW-7-M-246 Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage\nDoyoung Kwon, Jaesin Ahn, Jaeil Kim, Inchul Choi, Sungmoon Jeong, Young-Sup Lee, Jaechan Park, Minho Lee\nW-7-M-255 Realistic Breast Mass Generation through BIRADS Category\nHakmin Lee, Seong Tae Kim, Jae-Hyeok Lee, Yong Man Ro\nW-7-M-264 Triple ANet: Adaptive Abnormal-aware Attention Network for WCE Image Classfication\nXiaoqing Guo, Yixuan Yuan\nW-7-M-276 Normal appearance autoencoder for lung cancer detection and segmentation\nMehdi Astaraki, Iuliana Toma Dasu, Örjan Smedby, Chunliang Wang\nW-7-M-285 Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss\nMhd Hasan Sarhan, Shadi Albarqouni, Mehmet Yigitsoy, Nassir Navab, Abouzar Eslami\nW-7-M-294 Learning from Longitudinal Mammography Studies\nShaked Perek, Lior Ness, Mika Amit, Ella Barkan, Guy Amit\nW-7-M-306 Weakly Supervised Universal Fracture Detection in Pelvic X-rays\nYirui Wang, Le Lu, Chi Tung Cheng, Dakai Jin, Adam Harrison, Jing Xiao, Chienhung Liao, Shun Miao\nW-7-M-324 Adaptive Sparsity Regularization Based Collaborative Clustering for Cancer Prognosis\nHangfan Liu, Hongming Li, Yuemeng Li, Shi Yin, Pamela Boimel, James Janopaul-Naylor, Haoyu Zhong, Ying Xiao, Edgar Ben-Josef, Yong Fan\nW-7-F-349 Texture-based classification of significant stenosis in CCTA multi-view images of coronary arteries\nAntonio Tejero-De-Pablos, Kaikai Huang, Hiroaki Yamane, Yusuke Kurose, Yusuke Mukuta, Yasushi Koyama, Junichi Iho, Youji Tokunaga, Makoto Horie, Keisuke Nishizawa, Yusaku Hayashi, Tatsuya Harada\nW-7-F-358 A Surface-theoretic Approach for Statistical Shape Modeling\nFelix Ambellan, Stefan Zachow, Christoph Von Tycowicz\nW-7-F-367 How to learn from unlabeled volume data: Self-Supervised 3D Context Feature Learning\nMaximilian Blendowski, Hannes Nickisch, Mattias Heinrich\nW-7-F-382 Retinal Abnormalities Recognition Using Regional Multitask Learning\nXin Wang, Lie Ju, Xin Zhao, Zongyuan Ge\nW-7-F-388 Refined Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants\nYalong Liu, Jie Li, Ying Wang, Miaomiao Wang, Xianjun Li, Zhicheng Jiao, Jian Yang, Xinb Gao\nW-7-F-396 Semi-Supervised Learning by Disentangling and Self-Ensembling over Stochastic Latent Space\nPrashnna Gyawali, Zhiyuan Li, Sandesh Ghimire, Linwei Wang\nW-7-F-410 A Deep Learning Design for improving Topology Coherence in Blood Vessel Segmentation\nRicardo Araújo, Jaime Cardoso, Hélder Oliveira\nW-7-F-424 Learning Interpretable Features via Adversarially Robust Optimization\nAshkan Khakzar, Shadi Albarqouni, Nassir Navab\nW-7-F-431 Adaptive image-feature learning for disease classification using inductive graph networks\nHendrik Burwinkel, Anees Kazi, Gerome Vivar, Shadi Albarqouni, Guillaume Zahnd, Nassir Navab, Ahmad Ahmadi\nW-7-E-441 Spatial-Frequency Non-Local Convolutional LSTM Network for pRCC classification\nYu Zhao, Yuan Liu, Yansheng Kan, Anjany Sekuboyina, Diana Waldmannstetter, Hongwei Li, Xiaobin Hu, Xiaozhi Zhao, Kuangyu Shi, Bjoern Menze\nW-7-E-465 Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data\nMaayan Frid-Adar, Rula Amer, Hayit Greenspan\nW-7-E-474 Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation\nW-7-E-483 STructural Rectal Atlas Deformation (StRAD) features for characterizing intra- and peri-wall chemoradiation response on MRI\nJacob Antunes, Zhouping Wei, Charlems Alvarez-Jiminez, Marwa Ismail, Dr. Romero, Anant Madabushi, Pallavi Tiwari, Satish Viswanath\nW-7-E-489 Response Estimation through Spatially Oriented Neural Network and Texture Ensemble (RESONATE)\nJeffrey Eben, Nathaniel Braman, Anant Madabushi\nW-7-E-498 Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays\nRicardo Bigolin Lanfredi, Joyce Schroeder, Clement Vachet, Tolga Tasdizen\nW-7-E-507 Dual-view Joint Estimation of Left Ventricular Ejection Fraction with Uncertainty Modelling in Echocardiograms\nDelaram Behnami, Zhibin Liao, Hany Girgis, Christina Luong, Ken Gin, Robert Rohling, Terasa Tsang, Purang Abolmaesumi\nSession 8: Computer Assisted Interventions\nWednesday, October 16, 13:00 – 14:00\nW-8-B-009 Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field\nDaochang Liu, Tingting Jiang, Yizhou Wang, Rulin Miao, Fei Shan, Ziyu Li\nW-8-B-018 Hard Frame Detection and Online Mapping for Surgical Phase Recognition\nFangqiu Yi, Tingting Jiang\nW-8-B-027 Discriminative Correlation Filter Network for Robust Landmark Tracking in Ultrasound Guided Intervention\nChunxu Shen, Jishuai He, Yibin Huang, Jian Wu\nW-8-B-036 Model-Based Recommendations for Optimal Surgical Placement of Epiretinal Implants\nW-8-B-052 Haptic Modes for Multiparameter Control in Robotic Surgery\nPhilipp Schleer, Sergey Drobinsky, Tahany Hmaid, Klaus Radermacher\nW-8-B-061 Real-time Surface Deformation Recovery from Stereo Videos\nHaoyin Zhou, Jayender Jagadeesan\nW-8-B-066 Learning-based X-ray Image Denoising utilizing Model-based Image Simulations\nSai Gokul Hariharan, Norbert Strobel, Christian Kaethner, Markus Kowarschik, Shadi Albarqouni, Rebecca Fahrig, Nassir Navab\nW-8-B-075 Towards fully automatic X-ray to CT registration\nJavier Esteban, Matthias Grimm, Mathias Unberath, Guillaume Zahnd, Nassir Navab\nW-8-B-095 An Automatic Approach to Reestablish Final Dental Occlusion for 1-Piece Maxillary Orthognathic Surgery\nHan Deng, Peng Yuan, Sonny Wong, Jaime Gateno, Fred Garrett, Randy Ellis, Jeryl English, Helder Jacob, Daeseung Kim, James Xia\nW-8-B-104 Multi-task Localization and Segmentation for X-ray Guided Planning in Knee Surgery\nFlorian Kordon, Peter Fischer, Maxim Privalov, Benedict Swartman, Marc Schnetzke, Jochen Franke, Ruxandra Lasowski, Andreas Maier, Holger Kunze\nW-8-B-118 A New Approach of Predicting Facial Changes following Orthognathic Surgery using Realistic Lip Sliding Effect\nDaeseung Kim, Tianshu Kuang, Yriu Rodrigues, Jaime Gateno, Steve G.F. Shen, Xudong Wang, Han Deng, Peng Yuan, David M. Alfi, Michael A.K. Liebschner, James Xia\nW-8-B-140 Interactive Endoscopy: A Next-Generation, Streamlined User Interface for Lung Surgery Navigation\nPaul Thienphrapa, Torre Bydlon, Alvin Chen, Prasad Vagdargi, Nicole Varble, Douglas Stanton, Aleksandra Popovic\nW-8-B-154 User guidance for point-of-care echocardiography using a multi-task deep neural network\nGrzegorz Toporek, Raghavendra Srinivasa Naidu, Adriana Simicich, Tony Gades, Hua Xie, Balasundar Raju\nW-8-M-168 Variational Shape Completion for Virtual Planning of Jaw Reconstructive Surgery\nAmir Abdi, Mehran Pesteie, Eitan Prisman, Purang Abolmaesumi, Sidney Fels\nW-8-M-184 Single Shot Needle Tip Localization in 2D Ultrasound\nCosmas Mwikirize, John L Nosher, Ilker Hacihaliloglu\nW-8-M-197 Free-view, 3D Gaze-Guided Robotic Scrub Nurse\nAlexandros Kogkas, Ahmed Ezzat, Rudrik Thakkar, Ara Darzi, George Mylonas\nW-8-M-206 A Mesh-Aware Ball-Pivoting Algorithm for Generating the Virtual Arachnoid Mater\nHirofumi Seo, Taichi Kin, Takeo Igarashi\nW-8-M-214 Transferring from ex-vivo to in-vivo: Instrument Localization in 3D Cardiac Ultrasound Using Pyramid-UNet with Hybrid Loss\nHongxu Yang, Caifeng Shan, Tao Tan, Alexander F. Kolen, P. H. N. De With\nW-8-M-227 Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation\nW-8-M-236 Triplet Feature Learning on Endoscopic Video Manifold for Real-time Gastrointestinal Image Retargeting\nYun Gu, Benjamin Walter, Jie Yang, Alexander Meining, Guang-Zhong Yang\nW-8-M-244 Pathology-aware deep network visualization and its application in glaucoma image synthesis\nXiaofei Wang, Mai Xu, Liu Li, Zulin Wang, Zhenyu Guan\nW-8-M-257 Direct Visual and Haptic Volume Rendering of Medical Data Sets for an Immersive Exploration in Virtual Reality\nBalázs Faludi, Esther Zoller, Nicolas Gerig, Azhar Zam, Georg Rauter, Philippe C. Cattin\nW-8-M-266 Towards a first mixed-reality first person point of view needle navigation system\nLeah Groves, Natalie Li, Terence M Peters, Elvis Chen\nW-8-M-274 Automated Surgical Activity Recognition with One Labeled Sequence\nW-8-M-287 ARAMIS: Augmented Reality Assistance for Minimally Invasive Surgery using a Head-Mounted Display\nLong Qian, Xiran Zhang, Anton Deguet, Peter Kazanzides\nW-8-M-296 W-8-F-343 Development of an Multi-objective Optimized Planning Method for Microwave Liver Tumor Ablation\nLibin Liang, Derek Cool, Nirmal Kakani, Guangzhi Wang, Hui Ding, Aaron Fenster\nW-8-M-304 One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloud\nXiao-Yun Zhou, Zhao-Yang Wang, Pei-Chao Li, Jian-Qing Zheng, Guang-Zhong Yang\nW-8-M-317 Towards Multiple Instance Learning and Hermann Weyl’s Discrepancy for Robust Image-Guided Bronchoscopic Intervention\nW-8-M-326 A Novel Endoscopic Navigation System: Simultaneous Endoscope and Radial Ultrasound Probe Tracking Without External Trackers\nW-8-M-337 Learning to Detect Collisions for Continuum Manipulators without a Prior Model\nShahriar Sefati, Shahin Sefati, Iulian Iordachita, Russ Taylor, Mehran Armand\nW-8-F-351 Continually Modeling Alzheimer’s Disease Progression via Deep Multi-Order Preserving Weight Consolidation\nJie Zhang, Yalin Wang\nW-8-F-360 Optimizing Clearance of Bézier Spline Trajectories for Minimally-Invasive Surgery\nJohannes Fauser, Igor Stenin, Julia Kristin, Thomas Klenzner, Jörg Schipper, Anirban Mukhopadhyay\nW-8-F-369 Deep Sequential Mosaicking of Fetoscopic Videos\nSophia Bano, Francisco Vasconcelos, Marcel Tella Amo, George Dwyer, Caspar Gruijthuijsen, Tom Vercauteren, Sebastien Ourselin, Emmanuel Vanderpoorten, Jan Deprest, Danail Stoyanov\nW-8-F-384 A generative model of hyperelastic strain energy density functions for real-time simulation of brain tissue deformation\nAlejandro Granados, Martin Schweiger, Vejay Vakharia, Andrew W. Mcevoy, Anna Miserocchi, John Duncan, Rachel Sparks, Sebastien Ourselin\nW-8-F-398 Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images\nW-8-F-412 An Integrated Multi-Physics Finite Element Modeling Framework for Deep Brain Stimulation: Preliminary Study on Impact of Brain Shift on Neuronal Pathways\nMa Luo, Paul Larson, Alastair Martin, Peter Konrad, Michael Miga\nW-8-F-417 Simulation of Balloon-Expandable Coronary Stent Apposition with Plastic Beam Elements\nCamille Krewcun, Laurent Sarry, Nicolas Combaret, Pascal Motreff, Émilie Péry\nW-8-F-426 Using 3D Convolutional Neural Networks to learn spatiotemporal features for automatic surgical gesture recognition in video\nW-8-F-433 Landmark-guided Deformable Image Registration for Supervised Autonomous Robotic Tumor Resection\nJiawei Ge, Hamed Saeidi, Justin Opfermann, Arjun Joshi, Axel Krieger\nW-8-E-463 3D Modelling of the residual freezing for renal cryoablation simulation and prediction\nCaroline Essert, Pramod P. Rao, Afshin Gangi, Leo Joskowicz\nW-8-E-472 Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation\nMicha Pfeiffer, Isabel Funke, Maria Robu, Sebastian Bodenstedt, Leon Strenger, Sandy Engelhardt, Tobias Ross, Matthew Clarkson, Kurinchi Gurusamy, Brian Davidson, Lena Mair-Hein, Carina Riediger, Thilo Welsch, Jürgen Weitz, Stefanie Speidel\nW-8-E-481 Physics-based Deep Neural Network for Augmented Reality during Liver Surgery\nJean-Nicolas Brunet, Andrea Mendizabal, Antoine Petit, Nicolas Golse, Eric Vibert, Stephane Cotin\nW-8-E-491 Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training\nSandy Engelhardt, Lalith Sharan, Matthias Karck, Raffale De Simone, Ivo Wolf\nW-8-E-500 Conditional Segmentation in Lieu of Image Registration\nYipeng Hu, Eli Gibson, Dean Barratt, Mark Emberton, Alison Noble, Tom Vercauteren\nW-8-E-509 Investigating the role of VR in a simulation-based medical planning system for coronary interventions\nMadhurima Vardhan, Harvey Shi, John Gounley, S. James Chen, Andrew Kahn, Jane Leopold, Amanda Randles\nSession 9: MIC meets CAI\nWednesday, October 16, 15:30 – 16:30\nW-9-B-008 ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation\nChaoyue Liu, Guohao Dong, Muqing Lin, Yaoxian Zou, Tianzhu Liang, Xujin He, Zhijie Chen, Dong Ni, Yi Xiong, Lei Zhu\nW-9-B-017 Overcoming Data Limitation in Medical Visual Question Answering\nBinh Nguyen, Binh Nguyen, Tuong Do, Erman Tjiputra, Quang Tran, Thanh-Toan Do\nW-9-B-026 Hand Pose Estimation for Pediatric Bone Age Assessment\nMaria Escobar, Cristina González, Felipe Torres Figueroa, Laura Daza, Gustavo Triana, Pablo Arbelaez\nW-9-B-035 Generalized Non-Rigid Point Set Registration with Hybrid Mixture Models Considering Anisotropic Positional Uncertainties\nZhe Min, Li Liu, Max Q.-H. Meng\nW-9-B-051 Synthesis and Inpainting-based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors\nDongming Wei, Sahar Ahmad, Jiayu Huo, Yunhao Ge, Wen Peng, Pew-Thian Yap, Zhong Xue, Dinggang Shen, Qian Wang\nW-9-B-060 Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis\nJiancheng Yang, Rongyao Fang, Bingbing Ni, Yamin Li, Yi Xu, Linguo Li\nW-9-B-074 Biomarker Localization by Combining CNN Classifier and Generative Adversarial Network\nRong Zhang, Shuhan Tan, Ruixuan Wang, Siyamalan Manivannan, Jingjing Chen, Haotian Lin, Wei-Shi Zheng\nW-9-B-083 Learning Where to Look While Tracking Instruments in Robot-assisted Surgery\nMobarakol Islam, Yueyuan Li, Hongliang Ren\nW-9-B-087 LumiPath – Towards Real-time Physically-based Rendering on Embedded Devices\nLaura Fink, Sing Chun Lee, Jie Ying Wu, Xingtong Liu, Tianyu Song, Yordanka Velikova, Marc Stamminger, Nassir Navab, Mathias Unberath\nW-9-B-096 Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories\nJan-Nico Zaech, Cong Gao, Bastian Bier, Russ Taylor, Andreas Maier, Nassir Navab, Mathias Unberath\nW-9-B-105 MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation\nTianchen Wang, Jinjun Xiong, Xiaowei Xu, Meng Jiang, Haiyun Yuan, Meiping Huang, Jian Zhuang, Yiyu Shi\nW-9-B-117 An Extremely Fast and Precise Convolutional Neural Network for Recognition and Localization of Cataract Surgical Tools\nDongqing Zang, Gui-bin Bian, Yunlai Wang, Zhen Li\nW-9-B-127 FetusMap: Fetal Pose Estimation in 3D Ultrasound\nXin Yang, Wenlong Shi, Haoran Dou, Jikuan Qian, Yi Wang, Wufeng Xue, Shengli Li, Dong Ni, Pheng-Ann Heng\nW-9-B-141 Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound\nHaoran Dou, Xin Yang, Jikuan Qian, Wufeng Xue, Hao Qin, Xu Wang, Lequan Yu, Shujun Wang, Yi Xiong, Pheng-Ann Heng, Dong Ni\nW-9-B-155 Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video\nYueming Jin, Keyun Cheng, Qi Dou, Pheng-Ann Heng\nW-9-M-185 Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks\nJeffrey Craley, Christophe Jouny, Emily Johnson, Archana Venkataraman\nW-9-M-196 Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects\nDeqiang Xiao, Li Wang, Han Deng, Kim-Han Thung, Jihua Zhu, Peng Yuan, Yriu L. Rodrigues, Leonel Perez, Christopher E. Crecelius, Jaime Gateno, Tianshu Kuang, Steve G.F. Shen, Daeseung Kim, David M. Alfi, Pew-Thian Yap, James Xia, Dinggang Shen\nW-9-M-205 Non-invasive Assessment of In Vivo Auricular Cartilage by Ultrashort Echo Time (UTE) T2* Mapping\nXue Li, Cheng Zhao, Weiwei Zhang\nW-9-M-215 A Two-stage Framework for Real-time Guidewire Endpoint Localization\nRui-Qi Li, Guibin Bian, Xiaohu Zhou, Xiaoliang Xie, Zhenliang Ni, Zengguang Hou\nW-9-M-226 A Multi-modal Network for Cardiomyopathy Death Risk Prediction with CMR Images and Clinical Information\nChaoyang Xia, Xiaojie Li, Xin Wang, Bin Kong, Yucheng Chen, Youbing Yin, Kunlin Cao, Qi Song, Siwei Lyu, Xi Wu\nW-9-M-235 DME-Net: Diabetic Macular Edema Grading by Auxiliary Task Learning\nXiaodong He, Yi Zhou, Boyang Wang, Shanshan Cui, Ling Shao\nW-9-M-245 Multiple Landmarks Detection using Multi-Agent Reinforcement Learning\nW-9-M-256 Learning and Understanding Deep Spatio-Temporal Representations from Free-Hand Fetal Ultrasound Sweeps\nYuan Gao, Alison Noble\nW-9-M-265 Captioning Ultrasound Images Automatically\nMohammad Alsharid, Harshita Sharma, Lior Drukker, Pierre Chatelain, Aris Papageorghiou, Alison Noble\nW-9-M-275 Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation\nArijit Patra, Yifan Cai, Pierre Chatelain, Harshita Sharma, Lior Drukker, Aris Papageorghiou, Alison Noble\nW-9-M-286 Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers\nW-9-M-295 Virtual Cardiac Surgical Planning through Hemodynamics Simulation and Design Optimization of Fontan Grafts\nByeol Kim, Yue-Hin Loke, Florence Stevenson, Dominik Siallagan, Paige Mass, Justin Opfermann, Narutoshi Hibino, Laura Olivieri, Axel Krieger\nW-9-M-305 Real-time 3D reconstruction of colonoscopic surfaces for determining missing regions\nRuibin Ma, Rui Wang, Sarah Mcgill, Julian Rosenman, Stephen Pizer, Jan-Michael Frahm\nW-9-M-316 MeshSNet: Deep Multi-Scale Mesh Feature Learning for End-to-End Tooth Labeling on 3D Dental Surfaces\nChunfeng Lian, Li Wang, Tai-Hsien Wu, Mingxia Liu, Francisca Durán, Ching-Chang Ko, Dinggang Shen\nW-9-M-325 A deep regression model for seed localization in prostate brachytherapy\nYading Yuan, Ren-Dih Sheu, Luke Fu, Yeh-Chi Lo\nW-9-M-338 Harmonic Balance Techniques in Cardiovascular Fluid Mechanics\nTaha Sabri Koltukluoğlu, Gregor Cvijetic, Ralf Hiptmair\nW-9-F-350 Fourier Spectral Dynamic Data Assimilation: Interlacing CFD with 4D flow MRI\nTaha Sabri Koltukluoğlu\nW-9-F-359 Generating Pareto optimal dose distributions for radiation therapy treatment planning\nDan Nguyen, Azar Sadeghnejad Barkousaraie, Chenyang Shen, Xun Jia, Steve Jiang\nW-9-F-368 Brain Dynamics Through the Lens of Statistical Mechanics by Unifying Structure and Function\nIgor Fortel, Mitchell Butler, Laura Korthauer, Liang Zhan, Yanfu Zhang, Lei Guo, Heng Huang, Ira Driscoll, Olusola Ajilore, Anastasios Sidiropoulos, Dan Schonfeld, Alex Leow\nW-9-F-383 Fetal Pose Estimation in Volumetric MRI using 3D Convolution Neural Network\nJunshen Xu, Molin Zhang, Esra Abaci Turk, Larry Zhang, Ellen Grant, Kui Ying, Polina Golland, Elfar Adalsteinsson\nW-9-F-389 Towards whole placenta segmentation at late gestation using multi-view ultrasound images\nVeronika Zimmer, Alberto Gomez, Emily Skelton, Nicolas Toussaint, Tong Zhang, Bishesh Khanal, Robert Wright, Yohan Noh, Alison Ho, Jacqueline Matthew, Joseph Hajnal, Julia Schnabel\nW-9-F-397 Gastric cancer detection from endoscopic images using synthesis by GAN\nW-9-F-411 Markerless Image-to-Face Registration for Untethered Augmented Reality in Head and Neck Surgery\nChristina Gsaxner, Antonio Pepe, Jürgen Wallner, Dieter Schmalstieg, Jan Egger\nW-9-F-425 Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization\nJwala Dhamala, Sandesh Ghimire, John L. Sapp, Bohumil Milan Horacek, Linwei Wang\nW-9-F-432 Attenuation Imaging with Pulse-Echo Ultrasound based on an Acoustic Reflector\nRichard Rau, Ozan Unal, Dieter Schweizer, Valery Vishnevskiy, Orcun Goksel\nW-9-E-446 A Sparsely Distributed Intra-cardial Ultrasonic Array for Real-time Endocardial Mapping\nAlon Baram, Hayit Greenspan, Zvi Friedman\nW-9-E-464 An unsupervised domain adaptation approach to classification of stem cell-derived cardiomyocytes\nCarolina Pacheco, Rene Vidal\nW-9-E-473 Deep Learning based Metal Artifacts Reduction in post-operative Cochlear Implant CT Imaging\nZihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Raffaelli Charles, Nicolas Guevara, Herve Delingette\nW-9-E-482 Semi-autonomous Robotic Anastomoses of Vaginal Cuffs using Marker Enhanced 3D Imaging and Path Planning\nMichael Kam, Hamed Saeidi, Shuwen Wei, Justin Opfermann, Simon Leonard, Michael Hsieh, Jin Kang, Axel Krieger\nW-9-E-490 Frame Rate Up-Conversion in Echocardiography Using a Conditioned Variational Autoencoder and Generative Adversarial Model\nFatemeh Taheri Dezaki, Hany Girgis, Robert Rohling, Ken Gin, Purang Abolmaesumi, Terasa Tsang\nW-9-E-499 Deep Learning-based Universal Beamformer for Ultrasound Imaging\nW-9-E-508 Doubly Weak Supervision of Deep Learning Models for Head CT\nKhaled Saab, Jared Dunnmon, Roger Goldman, Alexander Ratner, Hersh Sagreiya, Christopher Re, Daniel Rubin


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url: =Buy Tickets for Zhou Zhengzhong and Xu Hong Perform Schwanengesang in Shanghai | SmartTicket.cn by SmartShanghai\nZhou Zhengzhong and Xu Hong Perform Schwanengesang\nZhou Zhengzhong and Xu Hong will be performing "Song of the Swan” at Shanghai Symphony Orchestra Concert Hall on May 7 at 7.15pm. Tracks that they will perform are Schubert's "Impromptu in E flat major, D. 899/2”, "Impromptu in G flat major, D. 899/3” and "Song of the Swan”. Tickets are 80-280rmb per person.\nSchubert:Impromptu in E-Flat major, D. 899 No. 2\nSchubert: Impromptu in G-Flat major, D. 899 No. 3\nSchubert:Schwanengesang, D. 957\nProgramme: Schubert:Impromptu in E-Flat...


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url: =Jianpu Technology Inc. Announces Strategic Investment | Markets Insider\nMar. 5, 2018, 04:50 AM\nView original content:http://www.prnewswire.com/news-releases/jianpu-technology-inc-announces-strategic-investment-300607923.html


url: =#FAANG | Startup One Stop\nAn international committee of foreign governments has requested that Zuckerberg appear before lawmakers to face inquiries, particularly into disinformation, election meddling and privacy issues. Zuckerberg refused. Read More...