Automated Gleason grading of prostate cancer tissue microarrays via deep learning

[1]  J. Epstein,et al.  Prostate cancer grading: a decade after the 2005 modified system , 2018, Modern Pathology.

[2]  Peter J. Wild,et al.  Comprehensive immunohistochemical analysis of PD-L1 shows scarce expression in castration-resistant prostate cancer , 2017, Oncotarget.

[3]  Michael V. McConnell,et al.  Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning , 2017, Nature Biomedical Engineering.

[4]  Andrew J. Schaumberg,et al.  D R A F T H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer , 2017 .

[5]  Andrew Y. Ng,et al.  CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.

[6]  Ron Kikinis,et al.  Large scale digital prostate pathology image analysis combining feature extraction and deep neural network , 2017, ArXiv.

[7]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[8]  Ruedi Aebersold,et al.  A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients , 2017, Scientific Data.

[9]  Aleksey Boyko,et al.  Detecting Cancer Metastases on Gigapixel Pathology Images , 2017, ArXiv.

[10]  Erwan Zerhouni,et al.  Heterogeneity characterization of immunohistochemistry stained tissue using convolutional autoencoder , 2017, Medical Imaging.

[11]  Sanchit Gupta,et al.  Convolutional neural networks for prostate cancer recurrence prediction , 2017, Medical Imaging.

[12]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[13]  Mats Andersson,et al.  Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score , 2017, Medical Imaging.

[14]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  D. Gleason,et al.  PREDICTION OF PROGNOSIS FOR PROSTATIC ADENOCARCINOMA BY COMBINED HISTOLOGICAL GRADING AND CLINICAL STAGING , 2017, The Journal of urology.

[16]  O. Stegle,et al.  Deep learning for computational biology , 2016, Molecular systems biology.

[17]  B. van Ginneken,et al.  Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis , 2016, Scientific Reports.

[18]  Claes Lundström,et al.  Towards grading gleason score using generically trained deep convolutional neural networks , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[19]  Joachim M. Buhmann,et al.  Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity , 2016, Scientific Reports.

[20]  Jonathan Epstein,et al.  Grading of prostatic adenocarcinoma: current state and prognostic implications , 2016, Diagnostic Pathology.

[21]  L. Egevad,et al.  A Contemporary Prostate Cancer Grading System: A Validated Alternative to the Gleason Score. , 2016, European urology.

[22]  B. Trock,et al.  Clinical Validation of the 2005 ISUP Gleason Grading System in a Cohort of Intermediate and High Risk Men Undergoing Radical Prostatectomy , 2016, PloS one.

[23]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Jonathan I. Epstein,et al.  WHO Classification of of Tumours of the Urinary System and Male Genital Organs , 2016 .

[27]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[28]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[29]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[30]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[31]  Fabio A. González,et al.  Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks , 2014, Medical Imaging.

[32]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[33]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[34]  Andrew Evans,et al.  Digital imaging in pathology: whole-slide imaging and beyond. , 2013, Annual review of pathology.

[35]  Joachim M. Buhmann,et al.  TMARKER: A free software toolkit for histopathological cell counting and staining estimation , 2013, Journal of pathology informatics.

[36]  Anant Madabhushi,et al.  Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer , 2012, BMC Bioinformatics.

[37]  Anil K. Jain,et al.  Prostate cancer grading: Gland segmentation and structural features , 2012, Pattern Recognit. Lett..

[38]  Joachim M. Buhmann,et al.  Computational Pathology: Challenges and Promises for Tissue Analysis , 2015, Comput. Medical Imaging Graph..

[39]  Andrew H. Beck,et al.  Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival , 2011, Science Translational Medicine.

[40]  G. Kristiansen,et al.  KPNA2 Expression Is an Independent Adverse Predictor of Biochemical Recurrence after Radical Prostatectomy , 2011, Clinical Cancer Research.

[41]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[42]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .