This paper describes the need and rationale for developing a phases model for guiding alcohol-problem prevention research . A phased approach to prevention research is consistent with such models developed for other health areas including heart disease , cancer , and drug testing . Such a model in alcohol prevention research can provide a means for ( 1 ) locating how far research has progressed along a continuum from basic or pre-intervention research to full implementation of preventive action , ( 2 ) identifying gaps in research , and ( 3 ) determining the level of empirical proof which exists for one or more prevention strategies prior to widespread dissemination . Colorectal polyps are important precursors to colon cancer , a major health problem . Colon capsule endoscopy is a safe and minimally invasive examination procedure , in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient . IntroductionThis paper 's aim is to develop a data warehouse from the integration of the files of three Brazilian health information systems concerned with the production of ambulatory and hospital procedures for cancer care , and cancer mortality . These systems do not have a unique patient identification , which makes their integration difficult even within a single system . At the same level of false positives , best previous method detected 72.6 % . We also show that probes with differing binding affinity both hinder differential expression detection and introduce artifacts in cancer-healthy tissue comparison . ConclusionsDetection and removal of such probes should be a routine step in Affymetrix data preprocessing . Abstract Aromatase inhibitors ( AIs ) represent a promising therapeutic class of anticancer agents against estrogen receptor-positive breast cancer . Bioactivity data on pIC 50 of 973 AIs were employed in the construction of quantitative structure-activity relationship ( QSAR ) models using COR relation And Logic ( CORAL ) software ( http://www.insilico.eu/coral ) in which molecular structures are represented by the simplified molecular input line entry system ( SMILES ) notation . Results : The system is illustrated for prostate and paranasal sinus cancer cases . In the prostate case , below a certain extent of rectal and bladder dose sparing , decreasing dose to one structure leads to increasing dose to the other structure . Staging aims ultimately to define disease as metastatic or non-metastatic . Combination chemotherapy , generally platinum-based plus etoposide or irinotecan , is the mainstay first-line treatment for metastatic small-cell lung cancer . For non-metastatic disease , evidence supports early concurrent thoracic radiotherapy . The intra-class correlation coefficient of the % -density for duplicate images was estimated to be 0.80 , using the regression model-derived threshold values , and 0.94 if estimated directly from the parameter estimates of the % -density prediction regression model . Therefore , with additional research , these mathematical models could be used to compute breast density objectively , automatically bypassing the HSM step , and could greatly facilitate breast cancer research studies . As this latest issue of the Journal of Adolescent and Young Adult Oncology completes this year ’s volume and we look to the year ahead , it gives me cause to be grateful to all the authors who submitted their manuscripts to JAYAO , to the reviewers who selflessly reviewed those manuscripts , to our editorial board for their reviews and assistance finding the right reviewers , and to all of our readers who collectively make up the adolescent and young adult ( AYA ) cancer healthcare professional — a huge thank you . As I evaluated the list of those who reviewed articles for JAYAO in 2013 , I was struck by the number of national and international expert reviewers we called upon to make these issues a reality — a wonderful reality that we have created a truly international collaborative effort that has provided an academic home for scholarly focus on AYA cancer issues . JAYAO ’s continued growth — as evidenced by the ever increasing number , quality , and variety of manuscripts that are being submitted by an increased variety of healthcare professionals and disciplines — should make all of us proud that the field of AYAO continues to be become more widely accepted and relevant to oncology as a whole . Cancer is a complex disease that results from alterations in genes that control the growth and division mechanisms of the cell . Identifying cancer-associated genes and pathways through which these genes influence cellular function is the key challenge in cancer re- search . This thesis proposes novel computational approaches that integrate data using networks at different scales . The input patches are randomly sampled over an entire image , which also includes the outside region of the FOV . section : 2 ) Skin Cancer Segmentation This dataset is taken from the Kaggle competition on skin lesion segmentation that occurred in 2017 First row shows the input images , second row is ground truth , and third row shows the segmentation outputs using R2U - Net . section : 4 ) Skin Cancer Lesion Segmentation In this implementation , this dataset is preprocessed with mean subtraction and normalized according to the standard deviation . section : E. Computational time The computational time for testing per sample is shown in Table V for blood vessel segmentation for retina images , skin cancer , and lung segmentation respectively . In this paper , we proposed an extension of the U - Net architecture using Recurrent Convolutional Neural Networks and Recurrent Residual Convolutional Neural Networks . PTC consists of chemical compounds where classes indicate carcinogenicity for male and female rats . NCI1 and NCI109 are chemical compounds screened for activity against non - small cell lung cancer and ovarian cancer cell lines . PROTEINS is a graph collection where nodes are secondary structure elements and edges indicate neighborhood in the amino - acid sequence or in 3D space . section : Introduction In the United States alone , it is estimated that 23 , 000 new cases of brain cancer will be diagnosed in 2015 . While gliomas are the most common brain tumors , they can be less aggressive ( i.e. low grade ) in a patient with a life expectancy of several years , or more aggressive ( i.e. high grade ) in a patient with a life expectancy of at most 2 years . cancer that begins in melanocytes of the skin is called melanoma . Oral cancer is a significant health problem throughout the world . Most oral cancers are identified at a later stage where , treatment becomes less successful . The method detected the dysplastic tissue with an average area under the curve ( AUC ) of 0.89 . The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection . Optical Coherence Tomography ( OCT ) can discriminate morphological tissue features important for oral cancer detection such as the presence or absence of basement membrane and epithelial thickness . We previously reported an OCT system employing a rotary-pullback catheter capable of in vivo , rapid , wide-field ( up to 90 x 2.5mm2 ) imaging in the oral cavity . The spectral distribution is difference for lesions of epithelial cells and normal cells after excited fluorescence . We have been developed the hyperspectral and multispectral techniques for oral cancer diagnosis in three generations . This research is the third generation . This study presents a portable noninvasive detection system which can get the image of specific spectral immediately , that promote to oral cancer diagnosis . According to research literature show the performance called autofluorescence of cells after exciting by specific wave band of light . Oral cancer affects the mouth and pharynx and has a high mortality rate due to its late discovery . Current methods of oral cancer treatment , such as chemoradiation and surgery , fail to provide better chances for survival , warranting an alternative approach . By running Medusa on a data fusion graph consisting of biological objects , we incorporate binary classification to model the algorithm 's association detection to discover compounds with the potential to mitigate the effects of oral cancer . We present results demonstrating how depth-resolved fluorescence and reflectance spectroscopy can non-invasively and objectively discriminate between normal and neoplastic oral mucosa with improved sensitivity and specificity , and describe their implications for improved oral cancer screening . Article not available . The information from the autofluorescence and white light image pair is extracted , calculated , and fused to feed the deep learning neural networks . We have investigated and compared the performance of different convolutional neural networks , transfer learning , and several regularization techniques for oral cancer classification . Our experimental results demonstrate the effectiveness of deep learning methods in classifying dual-modal images for oral cancer detection . Accordingly , the present study proposes an optical method using a Scanned Laser Pico-Projection system ( SLPP ) and Gray-Level Cooccurrence Matrix ( GLCM ) . The validity of the proposed method is demonstrated using five indexes for quantized criteria for oral cancer classifies . The results show that the cancerous pathological sections have the higher Contrast and Entropy , but the lower Correlation , Homogeneity , and Energy . The performance was then further demonstrated by imaging a hamster cheek pouch in vivo , and oral mucosa tissue both ex vivo and in vivo , all using safe and permissible exposure levels . Such a design can greatly facilitate the evaluation of FLIM for oral cancer imaging in vivo . PurposeOral cancer is a complex wide spread cancer , which has high severity . Using advanced technology and deep learning algorithm early detection and classification are made possible . Since clinical observations face to different fault for melanoma detection , the automatic diagnosis can help to increase the accuracy of detection . Reviewing the researches have done in skin cancer detection and providing the overview on automatic detection of skin cancer are the ultimate aims of this paper . It presents the literature on automatic skin cancer detection and describes the different steps of such process . The double Debye model can be used to capture the dielectric response of human skin in terahertz regime due to high water content in the tissue . The increased water proportion is widely considered as a biomarker of carcinogenesis , which gives rise of using this model in skin cancer detection . Therefore , the goal of this paper is to provide a specific analysis of the double Debye parameters in terms of non-melanoma skin cancer classification . Irregular streaks are important clues for Melanoma ( a potentially fatal form of skin cancer ) diagnosis using dermoscopy images . This paper extends our previous algorithm to identify the absence or presence of streaks in a skin lesions , by further analyzing the appearance of detected streak lines , and performing a three-way classification for streaks , Absent , Regular , and Irregular , in a pigmented skin lesion . Deep Neural Networks ( DNNs ) show a significant impact on medical imaging . One significant problem with adopting DNNs for skin cancer classification is that the class frequencies in the existing datasets are imbalanced . This problem hinders the training of robust and well-generalizing models . Computer-aided diagnosis ( CAD ) systems have been developed to lessen this burden and to help the patients to conduct the early assessment of the skin lesion . Mostly CAD systems available in the literature only provide skin cancer classification . Classification of the skin lesion is a challenging research area due to similar characteristics of skin lesions . And the early diagnosis of it is quite important . Aiming to improve the classification of skin cancer , a modified convolutional neural net- work model is proposed . Firstly , a convolutional layer and a pooling layer is added on the original network , constructed an 8-layer convolutional neural network . Terahertz imaging has been proposed for burns and skin cancer identification . However , the role of melanocytes , melanosomes , melanin content and distribution in determining the terahertz optical properties of human skin has not been investigated . Skin segmentation plays an important role in a wide variety of biomedical image processing applications , such as skin cancer identification , skin lesion detection , and wound isolation . However , contemporary research has been mainly based on facial and hand skin datasets , with no other body regions considered for skin pixels sampling . However , these networks require large amount of data that is a big challenge in medical domain where often have insufficient data and even a pretrained model on medical images can be hardly found . Lesion segmentation as the initial step of skin cancer analysis remains a challenging issue since datasets are small and include a variety of images in terms of light , color , scale , and marks which have led researchers to use extensive augmentation and preprocessing techniques or fine tuning the network with a pretrained model on irrelevant images . A segmentation model based on convolutional neural networks is proposed in this study for the tasks of skin lesion segmentation and dermoscopic feature segmentation . This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming ( GP ) method . Existing approaches have employed various feature extraction methods to extract features from skin cancer images , where these different types of features are used individually for skin cancer image classification . However they remain unable to use all these features together in a meaningful way to achieve performance gains . Feature selection and construction are effective techniques in reducing the dimensionality while improving classification performance . This work develops a novel genetic programming ( GP ) based two-stage approach to feature selection and feature construction for skin cancer image classification . Local binary pattern is used to extract gray and colour features from the dermoscopy images . In addition , the Inception Recurrent Residual Convolutional Neural Network ( IRRCNN ) model is used for skin cancer classification . The proposed NABLA-N network and IRRCNN models are evaluated for skin cancer segmentation and classification on the benchmark datasets from the International Skin Imaging Collaboration 2018 ( ISIC-2018 ) . The experimental results show superior performance on segmentation tasks compared to the Recurrent Residual U-Net ( R2U-Net ) . It involves extraction of many different features and human intervention . In this paper we propose a new approach to skin cancer classification problem based on power spectrum estimation in the frequency domain and demonstrates that significant changes between the two classes can be derived . The periodogram is a means for the spectrum estimation and effectively utilized here in . Bringing these different resources into a common framework , with a data model that allows for complex relationships as well as dense vectors of features , will unlock integrative analysis . We introduce a graph database and query engine for discovery and analysis of cancer biology , called the BioMedical Evidence Graph ( BMEG ) . The construction of the BMEG has resulted in a graph containing over 36 M vertices and 29 M edges . Hypotheses are generated regarding novel signalling links , some of which are validated by independent experiments . Third , we describe a network-based clustering approach for the discovery of cancer subtypes that differ in terms of subtype-specific signalling network structure . Model-based clustering is combined with penalised likelihood estimation of undirected graphical models to allow simultaneous learning of cluster assignments and cluster-specific network structure . An overview of translational , human oncogenomics , transcriptomics and cancer interactomic networks is presented together with basic concepts and potential , new applications to Oncology and Integrative Cancer Biology . Novel translational oncogenomics research is rapidly expanding through the application of advanced technology , research findings and computational tools/models to both pharmaceutical and clinical problems . Sparse reduced-rank regression ( Srrr ) method , a state-of-the-art multiple response linear regression method , can easily deal with high dimensional statistical data . In this paper , we introduced Srrr method combining iCluster ( Srrr-cluster ) to discovery cancer subtypes . Firstly , we used Srrr to estimate the coefficient matrix and then cancer subtypes were clustered by iCluster .
This paper describes the need and rationale for developing a phases model for guiding alcohol-problem prevention research . A phased approach to prevention research is consistent with such models developed for other health areas including heart disease , cancer , and drug testing . Such a model in alcohol prevention research can provide a means for ( 1 ) locating how far research has progressed along a continuum from basic or pre-intervention research to full implementation of preventive action , ( 2 ) identifying gaps in research , and ( 3 ) determining the level of empirical proof which exists for one or more prevention strategies prior to widespread dissemination .
Colorectal polyps are important precursors to colon cancer , a major health problem . Colon capsule endoscopy is a safe and minimally invasive examination procedure , in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient .
IntroductionThis paper 's aim is to develop a data warehouse from the integration of the files of three Brazilian health information systems concerned with the production of ambulatory and hospital procedures for cancer care , and cancer mortality . These systems do not have a unique patient identification , which makes their integration difficult even within a single system .
At the same level of false positives , best previous method detected 72.6 % . We also show that probes with differing binding affinity both hinder differential expression detection and introduce artifacts in cancer-healthy tissue comparison . ConclusionsDetection and removal of such probes should be a routine step in Affymetrix data preprocessing .
Abstract Aromatase inhibitors ( AIs ) represent a promising therapeutic class of anticancer agents against estrogen receptor-positive breast cancer . Bioactivity data on pIC 50 of 973 AIs were employed in the construction of quantitative structure-activity relationship ( QSAR ) models using COR relation And Logic ( CORAL ) software ( http://www.insilico.eu/coral ) in which molecular structures are represented by the simplified molecular input line entry system ( SMILES ) notation .
Results : The system is illustrated for prostate and paranasal sinus cancer cases . In the prostate case , below a certain extent of rectal and bladder dose sparing , decreasing dose to one structure leads to increasing dose to the other structure .
Staging aims ultimately to define disease as metastatic or non-metastatic . Combination chemotherapy , generally platinum-based plus etoposide or irinotecan , is the mainstay first-line treatment for metastatic small-cell lung cancer . For non-metastatic disease , evidence supports early concurrent thoracic radiotherapy .
The intra-class correlation coefficient of the % -density for duplicate images was estimated to be 0.80 , using the regression model-derived threshold values , and 0.94 if estimated directly from the parameter estimates of the % -density prediction regression model . Therefore , with additional research , these mathematical models could be used to compute breast density objectively , automatically bypassing the HSM step , and could greatly facilitate breast cancer research studies .
As this latest issue of the Journal of Adolescent and Young Adult Oncology completes this year ’s volume and we look to the year ahead , it gives me cause to be grateful to all the authors who submitted their manuscripts to JAYAO , to the reviewers who selflessly reviewed those manuscripts , to our editorial board for their reviews and assistance finding the right reviewers , and to all of our readers who collectively make up the adolescent and young adult ( AYA ) cancer healthcare professional — a huge thank you . As I evaluated the list of those who reviewed articles for JAYAO in 2013 , I was struck by the number of national and international expert reviewers we called upon to make these issues a reality — a wonderful reality that we have created a truly international collaborative effort that has provided an academic home for scholarly focus on AYA cancer issues . JAYAO ’s continued growth — as evidenced by the ever increasing number , quality , and variety of manuscripts that are being submitted by an increased variety of healthcare professionals and disciplines — should make all of us proud that the field of AYAO continues to be become more widely accepted and relevant to oncology as a whole .
Cancer is a complex disease that results from alterations in genes that control the growth and division mechanisms of the cell . Identifying cancer-associated genes and pathways through which these genes influence cellular function is the key challenge in cancer re- search . This thesis proposes novel computational approaches that integrate data using networks at different scales .
The input patches are randomly sampled over an entire image , which also includes the outside region of the FOV . section : 2 ) Skin Cancer Segmentation This dataset is taken from the Kaggle competition on skin lesion segmentation that occurred in 2017
First row shows the input images , second row is ground truth , and third row shows the segmentation outputs using R2U - Net . section : 4 ) Skin Cancer Lesion Segmentation In this implementation , this dataset is preprocessed with mean subtraction and normalized according to the standard deviation .
section : E. Computational time The computational time for testing per sample is shown in Table V for blood vessel segmentation for retina images , skin cancer , and lung segmentation respectively . In this paper , we proposed an extension of the U - Net architecture using Recurrent Convolutional Neural Networks and Recurrent Residual Convolutional Neural Networks .
PTC consists of chemical compounds where classes indicate carcinogenicity for male and female rats . NCI1 and NCI109 are chemical compounds screened for activity against non - small cell lung cancer and ovarian cancer cell lines . PROTEINS is a graph collection where nodes are secondary structure elements and edges indicate neighborhood in the amino - acid sequence or in 3D space .
section : Introduction In the United States alone , it is estimated that 23 , 000 new cases of brain cancer will be diagnosed in 2015 . While gliomas are the most common brain tumors , they can be less aggressive ( i.e. low grade ) in a patient with a life expectancy of several years , or more aggressive ( i.e. high grade ) in a patient with a life expectancy of at most 2 years .
cancer that begins in melanocytes of the skin is called melanoma . Oral cancer is a significant health problem throughout the world . Most oral cancers are identified at a later stage where , treatment becomes less successful .
The method detected the dysplastic tissue with an average area under the curve ( AUC ) of 0.89 . The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection .
Optical Coherence Tomography ( OCT ) can discriminate morphological tissue features important for oral cancer detection such as the presence or absence of basement membrane and epithelial thickness . We previously reported an OCT system employing a rotary-pullback catheter capable of in vivo , rapid , wide-field ( up to 90 x 2.5mm2 ) imaging in the oral cavity .
The spectral distribution is difference for lesions of epithelial cells and normal cells after excited fluorescence . We have been developed the hyperspectral and multispectral techniques for oral cancer diagnosis in three generations . This research is the third generation .
This study presents a portable noninvasive detection system which can get the image of specific spectral immediately , that promote to oral cancer diagnosis . According to research literature show the performance called autofluorescence of cells after exciting by specific wave band of light .
Oral cancer affects the mouth and pharynx and has a high mortality rate due to its late discovery . Current methods of oral cancer treatment , such as chemoradiation and surgery , fail to provide better chances for survival , warranting an alternative approach . By running Medusa on a data fusion graph consisting of biological objects , we incorporate binary classification to model the algorithm 's association detection to discover compounds with the potential to mitigate the effects of oral cancer .
We present results demonstrating how depth-resolved fluorescence and reflectance spectroscopy can non-invasively and objectively discriminate between normal and neoplastic oral mucosa with improved sensitivity and specificity , and describe their implications for improved oral cancer screening . Article not available .
The information from the autofluorescence and white light image pair is extracted , calculated , and fused to feed the deep learning neural networks . We have investigated and compared the performance of different convolutional neural networks , transfer learning , and several regularization techniques for oral cancer classification . Our experimental results demonstrate the effectiveness of deep learning methods in classifying dual-modal images for oral cancer detection .
Accordingly , the present study proposes an optical method using a Scanned Laser Pico-Projection system ( SLPP ) and Gray-Level Cooccurrence Matrix ( GLCM ) . The validity of the proposed method is demonstrated using five indexes for quantized criteria for oral cancer classifies . The results show that the cancerous pathological sections have the higher Contrast and Entropy , but the lower Correlation , Homogeneity , and Energy .
The performance was then further demonstrated by imaging a hamster cheek pouch in vivo , and oral mucosa tissue both ex vivo and in vivo , all using safe and permissible exposure levels . Such a design can greatly facilitate the evaluation of FLIM for oral cancer imaging in vivo .
PurposeOral cancer is a complex wide spread cancer , which has high severity . Using advanced technology and deep learning algorithm early detection and classification are made possible .
Since clinical observations face to different fault for melanoma detection , the automatic diagnosis can help to increase the accuracy of detection . Reviewing the researches have done in skin cancer detection and providing the overview on automatic detection of skin cancer are the ultimate aims of this paper . It presents the literature on automatic skin cancer detection and describes the different steps of such process .
The double Debye model can be used to capture the dielectric response of human skin in terahertz regime due to high water content in the tissue . The increased water proportion is widely considered as a biomarker of carcinogenesis , which gives rise of using this model in skin cancer detection . Therefore , the goal of this paper is to provide a specific analysis of the double Debye parameters in terms of non-melanoma skin cancer classification .
Irregular streaks are important clues for Melanoma ( a potentially fatal form of skin cancer ) diagnosis using dermoscopy images . This paper extends our previous algorithm to identify the absence or presence of streaks in a skin lesions , by further analyzing the appearance of detected streak lines , and performing a three-way classification for streaks , Absent , Regular , and Irregular , in a pigmented skin lesion .
Deep Neural Networks ( DNNs ) show a significant impact on medical imaging . One significant problem with adopting DNNs for skin cancer classification is that the class frequencies in the existing datasets are imbalanced . This problem hinders the training of robust and well-generalizing models .
Computer-aided diagnosis ( CAD ) systems have been developed to lessen this burden and to help the patients to conduct the early assessment of the skin lesion . Mostly CAD systems available in the literature only provide skin cancer classification . Classification of the skin lesion is a challenging research area due to similar characteristics of skin lesions .
And the early diagnosis of it is quite important . Aiming to improve the classification of skin cancer , a modified convolutional neural net- work model is proposed . Firstly , a convolutional layer and a pooling layer is added on the original network , constructed an 8-layer convolutional neural network .
Terahertz imaging has been proposed for burns and skin cancer identification . However , the role of melanocytes , melanosomes , melanin content and distribution in determining the terahertz optical properties of human skin has not been investigated .
Skin segmentation plays an important role in a wide variety of biomedical image processing applications , such as skin cancer identification , skin lesion detection , and wound isolation . However , contemporary research has been mainly based on facial and hand skin datasets , with no other body regions considered for skin pixels sampling .
However , these networks require large amount of data that is a big challenge in medical domain where often have insufficient data and even a pretrained model on medical images can be hardly found . Lesion segmentation as the initial step of skin cancer analysis remains a challenging issue since datasets are small and include a variety of images in terms of light , color , scale , and marks which have led researchers to use extensive augmentation and preprocessing techniques or fine tuning the network with a pretrained model on irrelevant images . A segmentation model based on convolutional neural networks is proposed in this study for the tasks of skin lesion segmentation and dermoscopic feature segmentation .
This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming ( GP ) method . Existing approaches have employed various feature extraction methods to extract features from skin cancer images , where these different types of features are used individually for skin cancer image classification . However they remain unable to use all these features together in a meaningful way to achieve performance gains .
Feature selection and construction are effective techniques in reducing the dimensionality while improving classification performance . This work develops a novel genetic programming ( GP ) based two-stage approach to feature selection and feature construction for skin cancer image classification . Local binary pattern is used to extract gray and colour features from the dermoscopy images .
In addition , the Inception Recurrent Residual Convolutional Neural Network ( IRRCNN ) model is used for skin cancer classification . The proposed NABLA-N network and IRRCNN models are evaluated for skin cancer segmentation and classification on the benchmark datasets from the International Skin Imaging Collaboration 2018 ( ISIC-2018 ) . The experimental results show superior performance on segmentation tasks compared to the Recurrent Residual U-Net ( R2U-Net ) .
It involves extraction of many different features and human intervention . In this paper we propose a new approach to skin cancer classification problem based on power spectrum estimation in the frequency domain and demonstrates that significant changes between the two classes can be derived . The periodogram is a means for the spectrum estimation and effectively utilized here in .
Bringing these different resources into a common framework , with a data model that allows for complex relationships as well as dense vectors of features , will unlock integrative analysis . We introduce a graph database and query engine for discovery and analysis of cancer biology , called the BioMedical Evidence Graph ( BMEG ) . The construction of the BMEG has resulted in a graph containing over 36 M vertices and 29 M edges .
Hypotheses are generated regarding novel signalling links , some of which are validated by independent experiments . Third , we describe a network-based clustering approach for the discovery of cancer subtypes that differ in terms of subtype-specific signalling network structure . Model-based clustering is combined with penalised likelihood estimation of undirected graphical models to allow simultaneous learning of cluster assignments and cluster-specific network structure .
An overview of translational , human oncogenomics , transcriptomics and cancer interactomic networks is presented together with basic concepts and potential , new applications to Oncology and Integrative Cancer Biology . Novel translational oncogenomics research is rapidly expanding through the application of advanced technology , research findings and computational tools/models to both pharmaceutical and clinical problems .
Sparse reduced-rank regression ( Srrr ) method , a state-of-the-art multiple response linear regression method , can easily deal with high dimensional statistical data . In this paper , we introduced Srrr method combining iCluster ( Srrr-cluster ) to discovery cancer subtypes . Firstly , we used Srrr to estimate the coefficient matrix and then cancer subtypes were clustered by iCluster .
Alcohol-Problem Prevention Research Policy: The Need for a Phases Research Model Automated Polyp Detection in Colon Capsule Endoscopy Integrating Brazilian health information systems in order to support the building of data warehouses ‘maskBAD’ – a package to detect and remove Affymetrix probes with binding affinity differences Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors MO‐D‐351‐07: A System to Dynamically Balance Dose Sparing Between Critical Structures in Intensity Modulated Radiation Therapy (IMRT) Small-cell lung cancer Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit. AYAO: Looking Back at 2013 and Ahead to 2014 and One of the Next Developments in the Field of AYAO-SAYAO. Multi-scale network analyses for cancer omics data Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation Learning Convolutional Neural Networks for Graphs Brain tumor segmentation with Deep Neural Networks Oral Cancer Early Detection and Stages UsingVarious Methods Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis Automated segmentation of oral mucosa from wide-field OCT images (Conference Presentation) Portable multispectral imaging system for oral cancer diagnosis Portable noninvasive system for oral cancer diagnosis Modeling association detection in order to discover compounds to inhibit oral cancer Fluorescence Imaging and Spectroscopy for Oral Cancer Detection Automatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning. Automation Characterization for Oral Cancer by Pathological Image Processing with Gray-Level Co-occurrence Matrix Handheld multispectral fluorescence lifetime imaging system for in vivo applications. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm Review on automatic early skin cancer detection High correlation of double Debye model parameters in skin cancer detection Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network A Computer-aided diagnosis system for classifying prominent skin lesions using machine learning Prediction of Skin Cancer Based on Convolutional Neural Network Terahertz spectroscopy of human skin tissue models with different melanin content. Adaptation and Evaluation of Deep Learning Techniques for Skin Segmentation on Novel Abdominal Dataset Transform domain representation-driven convolutional neural networks for skin lesion segmentation A Multi-tree Genetic Programming Representation for Melanoma Detection Using Local and Global Features Genetic Programming for Feature Selection and Feature Construction in Skin Cancer Image Classification Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks Power spectra based classification of cancerous nevoscope skin images Exploring Integrative Analysis using the BioMedical Evidence Graph Sparse graphical models for cancer signalling Translational Oncogenomics and Human Cancer Interactome Networks Srrr-cluster: Using Sparse Reduced-Rank Regression to Optimize iCluster
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ArXiv ICML Medical Image Anal. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging EMBC CEEC BIBE Australasian Conference on Artificial Intelligence PRICAI (1) ICIC (3)
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