However , despite the fact that the shape of a mass as well as the characteristics of its boundary are highly discriminative attributes for its diagnosis , we have not included shape and boundary features that are based on an explicit segmentation of the mass from the background tissue in the previously proposed CADx approach . Hence , we present a novel method for the segmentation of mammographic masses in this work and describe how we have integrated this segmentation module into our existent CADx system . The approach is based on the observation that the optical density of a mass is usually high near its core and decreases towards its boundary . In the context of image processing algorithms for mass detection in mammography , texture is a key feature to be used to distinguish abnormal tissue from normal tissue . Recently , a texture model based on a multivariate gaussian mixture was proposed , of which the parameters are lear-ned in an unsupervised way from the pixel intensities of images . This study analyzes the performance of a computer aided detection ( CAD ) scheme for mass detection in mammography . We investigate the trained parameters of the detection scheme before any further testing . This paper presents a methodology for automatic segmentation of masses in digital mammograms based on two principles : thresholding and evolutionary algorithm . As the staring point of the particles of the swarm , we used Otsu . Objective of segmentation is to simply modify the presentation of an image so that it becomes more significant and easier to study . Although many algorithms have been proposed yet to segment out the suspicious regions of mammograms , automatic segmentation of masses of improved quality is still considered to be difficult . This study introduces a novel marker controlled watershed algorithm for segmentation of mammograms to highlight the suspicious regions more distinctly . This algorithm can detect and segment the breast cancer abnormality without prior knowledge regarding its presence . Automated single seed point region growing is utilised in this algorithm to perform the mass detection and segmentation automatically . Comparison with commercial semi-automated segmentation application is performed . This paper presents a new method for mass detection and segmentation in mammography images . The extraction of the breast border is the first step . Automatic segmentation of mammographic mass is an important yet challenging task . Despite the great success of shape prior in biomedical image analysis , existing shape modeling methods are not suitable for mass segmentation . As an application , we consider a smoothness constraint for the segmented image as our a priori information , and solve a gray-level based segmentation problem . The feasibility of using the neural network architecture based on this optimization criterion for the segmentation of masses in mammograms is demonstrated . This paper discusses the method to automatic mass segmentation and analysis of mammogram for classification of benign or malignant tumor . The identification process is started with image enhancement through cropping to remove artifacts , then followed by increasing the contrast through Contrast Limited Adaptive Histogram Equalization ( CLAHE ) . Finally , all regions smaller than 50 voxels considered as false positives are removed , and the median filter smoothes the mass boundaries to obtain the final segmentation results . The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images . Using the detection Accuracy ( Acc ) , Sensitivity ( Sen ) , Specificity ( Spe ) , and area under the curve ( AUC ) as evaluation indexes , the Acc , Sen , Spe , and AUC for DBT mass segmentation in the entire experimental dataset is 0.871 , 0.869 , 0.882 , and 0.859 , respectively . Mass lesion segmentation on mammograms is a challenging task since mass lesions are usually embedded and hidden in varying densities of parenchymal tissue structures . In this article , we present a method for automatic delineation of lesion boundaries on digital mammograms . RETRIVAL CITRA MAMMOGRAM DENGAN PENDEKATAN NON-NEGATIVE MATRIX FACTORIZATION DAN DUAL-TREE COMPLEX WAVELET TRANSFORM - Image retrieval , mammogram , DT CWT , NMF . Current approaches perform labeling of work by manual operation results in time consumption . For analysis of mammogram image , people need to mark region of interesting ( ROI ) of a lesion in advance , such as mass , calcification , architectural distortion , and asymmetry . In this paper , we propose a scheme to automatically label lesion on mammogram images , it is realized by combining object detection with structured report interpretation . Before these two steps implementing a really important preprocessing step has to take place which is the detection or segmentation of the breast region from the background . The analysis of mammogram image can assist the radiologist for early detection of tumor and diagnosis the breast cancer effectively [ 3 ] . Computer aided can classify into two main groups : computer-aided detection ( CADe ) and computer-aided diagnosis ( CADx ) . Formed from a class of rational orthogonal wavelets ( ROWs ) , a 2-D image filter is constructed for prefiltering of the mammogram . The filtered image facilitates a simple binarisation of the mammogram . An initial breast contour is then extracted from the binarised image with a simple boundary scan technique . Abstract : The objective of this thesis is to design an automated microcalcification detection system to be used as an aid in radiologic mammogram interpretation . This research proposes the following methodology for clustered microcalcification detection . In this paper , we propose an approach to computationally perform mammograms images classification based on a combined classifier . The combined classifier , which is formed by Support Vector Machines and Naive Bayes machine learning , results in reliable image classification thus making it a valuable tool for mammogram diagnostics . Breast tissue density is an important risk factor in the detection of breast cancer It is also known that interpretation of mammogram lesions is more difficult in dense tissues Therefore , getting a preliminary tissue classification may aid in the subsequent process of breast lesion detection and analysis This article reviews several classification techniques for two datasets , both digitized screen-film ( SFM ) and full-field digital ( FFDM ) mammography , classified according to BIRADS categories It concludes with a hierarchical classification procedure based on k-NN combined with principal component analysis on texture features The results obtained classifying 1740 mammograms reflect up to 83 % of samples correctly classified The method is being integrated within a CADe system developed by the authors . The paper presents a framework for improvement of diagnostic effectiveness in diagnostic mammography . Main human-oriented causes lowering the diagnostic value of mammography have been identified as : variability in mammogram description and assessment , radiologist 's perception and interpretation errors . Means and tools addressing those problems are : ontology to standardize description and reduce the variability of interpretation because of increased objectivity , CAD and reference database as aiding tools in perception and diagnosis improvement . This scheme was designed to capture the implicit knowledge of experts ’ visual systems in which the learned knowledge was modeled as the PTPM , which was shown to provide relatively high correlation coefficient of 0.93 with experts ’ estimations in a single equipment study ( Senographe 2000D , GE ) . In this study , we evaluate the reliability of our SIGMAM method in follow-up mammogram examinations with respect to temporal stability and reproducibility . For evaluation of temporal stability , we selected 170 pairs of CC-view mammograms of 170 female patients taken with the same equipment ( Senographe 2000D , GE ) within one year from the breast cancer screening database in our institute . The interference pattern can be tuned to different applications depending on detector resolution . Our eventual goal is to achieve compact interferometry of the design lengths required for a clinical mammogram system . The experimental result gives 98 % accuracy of the classification of cancer classes using SVM classifier . From the result , it indicates that a curvelet transformation is a capable tool for analysis of digital mammogram . In this paper we describe our work on representing and constructing ontologies for breast mammography , which is part of planned set of comprehensive breast imaging and pathology ontologies designed to support the screening and diagnosis of breast cancer . We select DAML+OIL as the web-enabled ontology modelling language because of available support for description logic-based reasoning . Abstract Identifying abnormality using breast mammography is a challenging task for radiologists due to its nature . A more consistent and precise imaging based CAD system plays a vital role in the classification of doubtful breast masses . RATIONALE AND OBJECTIVES Accurate image segmentation for breast lesions is a critical step in computer-aided diagnosis systems . The objective of this study was to develop a robust method for the automatic segmentation of breast masses on mammograms to extract feasible features for computer-aided diagnosis systems . MATERIALS AND METHODS The data set used in this study consisted of 483 regions of interest extracted from 328 patients . Multiple Classifier Technique , which is a hybrid intelligent system , aims to improve the Classification accuracy rate over single classifiers . In this paper , we present an effective approach to breast mammogram analysis to modify the classification accuracy of ensemble neural networkin which we utilize BI-RADS features that were combined with patient 's age and subtlety value , which has been tested on a widely available Digital Database of Screening Mammography ( DDSM ) . In our proposed method , we created an ensemble cluster by using Bagging , AdaBoost , Rotation Forest and reached 92 % overall classification accuracy . Convolution Neural Network has become a preferred choice for images analysis including a mammogram . In this paper , we review various deep learning concepts applied to breast mammogram analysis and summarizes contributions to this field . We present a summary of the recent developments and a discussion about the best practices done using CNN in mammogram analysis and improvements that can be done in future research . Conclusion : The proposed algorithm is effective for automatic breast mass segmentation . Significance : First , the sketchy position of mass is fixed by PCNN , which guides the algorithm to define a flexibly initial contour for DRLSE . The underlying Multi U-net algorithm is based on convolutional neural networks . Under the Mayo Clinic Institutional Review Board protocol , a prospective study of the automatic segmentation of suspicious breast masses was performed . The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy . Non-linear classifiers , especially SVMs , have been proven superior to any linear equivalent . Breast mass classification of mammograms , based only on textural features , achieved an optimal score of 83.9 % , through SVM classifiers . As a result , we can characterize the difficulty of detecting small tumours in terms of local intensity contrast in x-ray images . In comparison , we also introduce cone-beam CT breast imaging for 3D breast volume representation , which offers advantages for breast mass segmentation and measurement . The discussion is demonstrated with an experiment with a breast surgical specimen . Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches , we propose end-to-end trained deep multiinstance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data . We explore three different schemes to construct deep multi-instance networks for whole mammogram classification . Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training . Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning ( MIL ) for labeling a set of instances/patches , we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs . We explore three different schemes to construct deep multi-instance networks for whole mammogram classification . Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations . Specifically , we first train CNN-based patch classifiers on segmentation masks of lesions in mammograms , and then use the learned features to initialize a scanning-based model that renders a decision on the whole image , trained end-to-end on outcome data . We demonstrate that our approach effectively handles the “ needle in a haystack ” nature of full-image mammogram classification , achieving 0.92 AUROC on the DDSM dataset . We are developing an 18 × 27 cm digital x-ray detector for whole-breast mammography based on CCD imaging technology [ 1 ] . In this paper we report the performance of a 10 × 20 cm , 2-CCD prototype we have built and tested . Automatic mammogram classification and mass segmentation play a critical role in a computer-aided mammogram screening system . In this work , we present a unified mammogram analysis framework for both whole-mammogram classification and segmentation . Our model is designed based on a deep U-Net with residual connections , and equipped with the novel hybrid deep supervision ( HDS ) scheme for end-to-end multi-task learning .
However , despite the fact that the shape of a mass as well as the characteristics of its boundary are highly discriminative attributes for its diagnosis , we have not included shape and boundary features that are based on an explicit segmentation of the mass from the background tissue in the previously proposed CADx approach . Hence , we present a novel method for the segmentation of mammographic masses in this work and describe how we have integrated this segmentation module into our existent CADx system . The approach is based on the observation that the optical density of a mass is usually high near its core and decreases towards its boundary .
In the context of image processing algorithms for mass detection in mammography , texture is a key feature to be used to distinguish abnormal tissue from normal tissue . Recently , a texture model based on a multivariate gaussian mixture was proposed , of which the parameters are lear-ned in an unsupervised way from the pixel intensities of images .
This study analyzes the performance of a computer aided detection ( CAD ) scheme for mass detection in mammography . We investigate the trained parameters of the detection scheme before any further testing .
This paper presents a methodology for automatic segmentation of masses in digital mammograms based on two principles : thresholding and evolutionary algorithm . As the staring point of the particles of the swarm , we used Otsu .
Objective of segmentation is to simply modify the presentation of an image so that it becomes more significant and easier to study . Although many algorithms have been proposed yet to segment out the suspicious regions of mammograms , automatic segmentation of masses of improved quality is still considered to be difficult . This study introduces a novel marker controlled watershed algorithm for segmentation of mammograms to highlight the suspicious regions more distinctly .
This algorithm can detect and segment the breast cancer abnormality without prior knowledge regarding its presence . Automated single seed point region growing is utilised in this algorithm to perform the mass detection and segmentation automatically . Comparison with commercial semi-automated segmentation application is performed .
This paper presents a new method for mass detection and segmentation in mammography images . The extraction of the breast border is the first step .
Automatic segmentation of mammographic mass is an important yet challenging task . Despite the great success of shape prior in biomedical image analysis , existing shape modeling methods are not suitable for mass segmentation .
As an application , we consider a smoothness constraint for the segmented image as our a priori information , and solve a gray-level based segmentation problem . The feasibility of using the neural network architecture based on this optimization criterion for the segmentation of masses in mammograms is demonstrated .
This paper discusses the method to automatic mass segmentation and analysis of mammogram for classification of benign or malignant tumor . The identification process is started with image enhancement through cropping to remove artifacts , then followed by increasing the contrast through Contrast Limited Adaptive Histogram Equalization ( CLAHE ) .
Finally , all regions smaller than 50 voxels considered as false positives are removed , and the median filter smoothes the mass boundaries to obtain the final segmentation results . The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images . Using the detection Accuracy ( Acc ) , Sensitivity ( Sen ) , Specificity ( Spe ) , and area under the curve ( AUC ) as evaluation indexes , the Acc , Sen , Spe , and AUC for DBT mass segmentation in the entire experimental dataset is 0.871 , 0.869 , 0.882 , and 0.859 , respectively .
Mass lesion segmentation on mammograms is a challenging task since mass lesions are usually embedded and hidden in varying densities of parenchymal tissue structures . In this article , we present a method for automatic delineation of lesion boundaries on digital mammograms .
RETRIVAL CITRA MAMMOGRAM DENGAN PENDEKATAN NON-NEGATIVE MATRIX FACTORIZATION DAN DUAL-TREE COMPLEX WAVELET TRANSFORM - Image retrieval , mammogram , DT CWT , NMF .
Current approaches perform labeling of work by manual operation results in time consumption . For analysis of mammogram image , people need to mark region of interesting ( ROI ) of a lesion in advance , such as mass , calcification , architectural distortion , and asymmetry . In this paper , we propose a scheme to automatically label lesion on mammogram images , it is realized by combining object detection with structured report interpretation .
Before these two steps implementing a really important preprocessing step has to take place which is the detection or segmentation of the breast region from the background . The analysis of mammogram image can assist the radiologist for early detection of tumor and diagnosis the breast cancer effectively [ 3 ] . Computer aided can classify into two main groups : computer-aided detection ( CADe ) and computer-aided diagnosis ( CADx ) .
Formed from a class of rational orthogonal wavelets ( ROWs ) , a 2-D image filter is constructed for prefiltering of the mammogram . The filtered image facilitates a simple binarisation of the mammogram . An initial breast contour is then extracted from the binarised image with a simple boundary scan technique .
Abstract : The objective of this thesis is to design an automated microcalcification detection system to be used as an aid in radiologic mammogram interpretation . This research proposes the following methodology for clustered microcalcification detection .
In this paper , we propose an approach to computationally perform mammograms images classification based on a combined classifier . The combined classifier , which is formed by Support Vector Machines and Naive Bayes machine learning , results in reliable image classification thus making it a valuable tool for mammogram diagnostics .
Breast tissue density is an important risk factor in the detection of breast cancer It is also known that interpretation of mammogram lesions is more difficult in dense tissues Therefore , getting a preliminary tissue classification may aid in the subsequent process of breast lesion detection and analysis This article reviews several classification techniques for two datasets , both digitized screen-film ( SFM ) and full-field digital ( FFDM ) mammography , classified according to BIRADS categories It concludes with a hierarchical classification procedure based on k-NN combined with principal component analysis on texture features The results obtained classifying 1740 mammograms reflect up to 83 % of samples correctly classified The method is being integrated within a CADe system developed by the authors .
The paper presents a framework for improvement of diagnostic effectiveness in diagnostic mammography . Main human-oriented causes lowering the diagnostic value of mammography have been identified as : variability in mammogram description and assessment , radiologist 's perception and interpretation errors . Means and tools addressing those problems are : ontology to standardize description and reduce the variability of interpretation because of increased objectivity , CAD and reference database as aiding tools in perception and diagnosis improvement .
This scheme was designed to capture the implicit knowledge of experts ’ visual systems in which the learned knowledge was modeled as the PTPM , which was shown to provide relatively high correlation coefficient of 0.93 with experts ’ estimations in a single equipment study ( Senographe 2000D , GE ) . In this study , we evaluate the reliability of our SIGMAM method in follow-up mammogram examinations with respect to temporal stability and reproducibility . For evaluation of temporal stability , we selected 170 pairs of CC-view mammograms of 170 female patients taken with the same equipment ( Senographe 2000D , GE ) within one year from the breast cancer screening database in our institute .
The interference pattern can be tuned to different applications depending on detector resolution . Our eventual goal is to achieve compact interferometry of the design lengths required for a clinical mammogram system .
The experimental result gives 98 % accuracy of the classification of cancer classes using SVM classifier . From the result , it indicates that a curvelet transformation is a capable tool for analysis of digital mammogram .
In this paper we describe our work on representing and constructing ontologies for breast mammography , which is part of planned set of comprehensive breast imaging and pathology ontologies designed to support the screening and diagnosis of breast cancer . We select DAML+OIL as the web-enabled ontology modelling language because of available support for description logic-based reasoning .
Abstract Identifying abnormality using breast mammography is a challenging task for radiologists due to its nature . A more consistent and precise imaging based CAD system plays a vital role in the classification of doubtful breast masses .
RATIONALE AND OBJECTIVES Accurate image segmentation for breast lesions is a critical step in computer-aided diagnosis systems . The objective of this study was to develop a robust method for the automatic segmentation of breast masses on mammograms to extract feasible features for computer-aided diagnosis systems . MATERIALS AND METHODS The data set used in this study consisted of 483 regions of interest extracted from 328 patients .
Multiple Classifier Technique , which is a hybrid intelligent system , aims to improve the Classification accuracy rate over single classifiers . In this paper , we present an effective approach to breast mammogram analysis to modify the classification accuracy of ensemble neural networkin which we utilize BI-RADS features that were combined with patient 's age and subtlety value , which has been tested on a widely available Digital Database of Screening Mammography ( DDSM ) . In our proposed method , we created an ensemble cluster by using Bagging , AdaBoost , Rotation Forest and reached 92 % overall classification accuracy .
Convolution Neural Network has become a preferred choice for images analysis including a mammogram . In this paper , we review various deep learning concepts applied to breast mammogram analysis and summarizes contributions to this field . We present a summary of the recent developments and a discussion about the best practices done using CNN in mammogram analysis and improvements that can be done in future research .
Conclusion : The proposed algorithm is effective for automatic breast mass segmentation . Significance : First , the sketchy position of mass is fixed by PCNN , which guides the algorithm to define a flexibly initial contour for DRLSE .
The underlying Multi U-net algorithm is based on convolutional neural networks . Under the Mayo Clinic Institutional Review Board protocol , a prospective study of the automatic segmentation of suspicious breast masses was performed . The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy .
Non-linear classifiers , especially SVMs , have been proven superior to any linear equivalent . Breast mass classification of mammograms , based only on textural features , achieved an optimal score of 83.9 % , through SVM classifiers .
As a result , we can characterize the difficulty of detecting small tumours in terms of local intensity contrast in x-ray images . In comparison , we also introduce cone-beam CT breast imaging for 3D breast volume representation , which offers advantages for breast mass segmentation and measurement . The discussion is demonstrated with an experiment with a breast surgical specimen .
Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches , we propose end-to-end trained deep multiinstance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data . We explore three different schemes to construct deep multi-instance networks for whole mammogram classification . Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training .
Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning ( MIL ) for labeling a set of instances/patches , we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs . We explore three different schemes to construct deep multi-instance networks for whole mammogram classification . Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations .
Specifically , we first train CNN-based patch classifiers on segmentation masks of lesions in mammograms , and then use the learned features to initialize a scanning-based model that renders a decision on the whole image , trained end-to-end on outcome data . We demonstrate that our approach effectively handles the “ needle in a haystack ” nature of full-image mammogram classification , achieving 0.92 AUROC on the DDSM dataset .
We are developing an 18 × 27 cm digital x-ray detector for whole-breast mammography based on CCD imaging technology [ 1 ] . In this paper we report the performance of a 10 × 20 cm , 2-CCD prototype we have built and tested .
Automatic mammogram classification and mass segmentation play a critical role in a computer-aided mammogram screening system . In this work , we present a unified mammogram analysis framework for both whole-mammogram classification and segmentation . Our model is designed based on a deep U-Net with residual connections , and equipped with the novel hybrid deep supervision ( HDS ) scheme for end-to-end multi-task learning .
Incorporating a segmentation routine for mammographic masses into a knowledge-based CADx approach Statistical Texture Model for mass Detection in Mammography Error investigation of models for improved detection of masses in screening mammography Automatic segmentation of masses in digital mammograms using particle swarm optimization and graph clustering Mammographic image segmentation by marker controlled watershed algorithm Auto-probing breast cancer mass segmentation for early detection Breast mass detection using bilateral filter and mean shift based clustering Mammographic Mass Segmentation with Online Learned Shape and Appearance Priors Neural network based segmentation using a priori image models A system for computer aided diagnosis of breast cancer based on mass analysis DBT Masses Automatic Segmentation Using U-Net Neural Networks A dual-stage method for lesion segmentation on digital mammograms. RETRIVAL CITRA MAMMOGRAM DENGAN PENDEKATAN NON-NEGATIVE MATRIX FACTORIZATION DAN DUAL-TREE COMPLEX WAVELET TRANSFORM Lesion Labeling on Mammogram by Combining Object Detection with Structured Report Interpretation Breast Cancer Diagnosis by CAD A New Contour Detection Approach in Mammogram Using Rational Wavelet Filtering and MRF Smoothing Clustered Microcalcification Detection Using Optimized Difference of Gaussians. Breast Cancer Classification of Mammograms using a Combined Classifier Automatic Breast Tissue Classification Based on BIRADS Categories A framework for integrated, diagnosis supporting interface for mammograms description: advantages and pitfalls Reliability of Breast Density Estimation in Follow-Up Mammograms: Repeatability and Reproducibility of a Fully Automated Areal Percent Density Method A Novel Phase Contrast X-ray System Breast Cancer Diagnosis using Curvelet and Regional Features From Lexicon To Mammographic Ontology: Experiences and Lessons Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature Hybrid segmentation of mass in mammograms using template matching and dynamic programming. Multiple classifier systems for breast mass classification A Study on Convolution Neural Network for Breast Cancer Detection Automated Breast Mass Segmentation using Pulse-Coupled Neural Network and Distance Regularized Level Set Evolution: A Coarse-to-fine Approach Automated and real-time segmentation of suspicious breast masses using convolutional neural network Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers Why should breast tumour detection go three dimensional? Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification A CCD Based Digital Detector for Whole-Breast Digital Mammography A Unified Mammogram Analysis Method via Hybrid Deep Supervision
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2009 2013 2005 2015 2017 2017 2010 2016 1997 2013 2020 2007 2012 2019 2014 2007 1996 2012 2010 2007 2014 2017 2019 2003 2018 2010 2014 2019 2016 2018 2006 2003 2016 2017 2017 1998 2018
Medical Imaging: Computer-Aided Diagnosis IWSSIP BIBM SIGMAP MICCAI (2) ICNN LifeTech DICTA Digital Mammography / IWDM Digital Mammography / IWDM Description Logics BIOIMAGING DLMIA/ML-CDS@MICCAI Digital Mammography / IWDM RAMBO+BIA+TIA@MICCAI
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