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One of the biggest challenges for applying machine learning to histopathology is weak supervision: whole-slide images have billions of pixels yet often only one global label.
Detection of breast micro-metastases in axillary lymph nodes by infrared micro-spectral imaging
Benjamin Bird, Kristi Bedrossian, Nora Laver, Miloš Miljković, Melissa J Romeo, and Max Diem · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Mitosis detection in breast cancer histology images with deep neural networks
Dan C Cireşan, Alessandro Giusti, Luca M Gambardella, and Jürgen Schmidhuber · 2013
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks
Thibaut Durand, Nicolas Thome, and Matthieu Cord · 2016
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Deep Learning for Identifying Metastatic Breast Cancer
Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Andrew H Beck, and Beth Israel · 2016
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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Nicolas Coudray, Paolo Santiago Ocampo, Theodore Sakellaropoulos, Navneet Narula, Matija Snuderl, David Fenyö, Andre L. Moreira, Narges Razavian, and Aristotelis Tsirigos · 2018
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Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach
Pierre Courtiol, Eric W. Tramel, Marc Sanselme, and Gilles Wainrib · 2018
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Attention-based deep multiple instance learning
Maximilian Ilse, Jakub M. Tomczak, and Max Welling · 2018
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Attention-based deep multiple instance learning
Maximilian Ilse, Jakub M. Tomczak, and Max Welling · 2018
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Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images
Joel Saltz, Rajarsi Gupta, Le Hou, Tahsin Kurc, Pankaj Singh, Vu Nguyen, Dimitris Samaras, Kenneth R Shroyer, Tianhao Zhao, Rebecca Batiste, et al · 2018
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Neural image compression for gigapixel histopathology image analysis
David Tellez, Geert Litjens, Jeroen van der Laak, and Francesco Ciompi · 2018
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Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Gabriele Campanella, Matthew G. Hanna, Luke Geneslaw, Allen Miraflor, Vitor Werneck Krauss Silva, Klaus J. Busam, Edi Brogi, Victor E. Reuter, David S. Klimstra, and Thomas J. Fuchs · 2019
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Histosegnet: Semantic segmentation of histological tissue type in whole slide images
Lyndon Chan, Mahdi S Hosseini, Corwyn Rowsell, Konstantinos N Plataniotis, and Savvas Damaskinos · 2019
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Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
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Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
Jakob Nikolas Kather, Alexander T. Pearson, Niels Halama, Dirk Jäger, Jeremias Krause, Sven H. Loosen, Alexander Marx, Peter Boor, Frank Tacke, Ulf Peter Neumann, Heike I. Grabsch, Takaki Yoshikawa, Hermann Brenner, Jenny Chang-Claude, Michael Hoffmeister, Christian Trautwein, and Tom Luedde · 2019
Improved Baselines with Momentum Contrastive Learning
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Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning
Amelie Echle, Heike Irmgard Grabsch, Philip Quirke, Piet A van den Brandt, Nicholas P West, Gordon GA Hutchins, Lara R Heij, Xiuxiang Tan, Susan D Richman, Jeremias Krause, et al · 2020
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Bootstrap Your Own Latent A New Approach to Self-Supervised Learning
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Pan-cancer image-based detection of clinically actionable genetic alterations
Jakob Nikolas Kather, Lara R. Heij, Heike I. Grabsch, Chiara Loeffler, Amelie Echle, Hannah Sophie Muti, Jeremias Krause, Jan M. Niehues, Kai A. J. Sommer, Peter Bankhead, Loes F. S. Kooreman, Jefree J. Schulte, Nicole A. Cipriani, Roman D. Buelow, Peter Boor, Nadina Ortiz-Brüchle, Andrew M. Hanby, Valerie Speirs, Sara Kochanny, Akash Patnaik, Andrew Srisuwananukorn, Hermann Brenner, Michael Hoffmeister, Piet A. van den Brandt, Dirk Jäger, Christian Trautwein, Alexander T. Pearson, and Tom Luedde · 2020
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Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding
Ming Y. Lu, Richard J. Chen, Jingwen Wang, Debora Dillon, and Faisal Mahmood · 2019
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Neural Image Compression for Gigapixel Histopathology Image Analysis
David Tellez, Geert Litjens, Jeroen van der Laak, and Francesco Ciompi · 2019
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Automated deep-learning system for gleason grading of prostate cancer using biopsies: a diagnostic study
Wouter Bulten, Hans Pinckaers, Hester van Boven, Robert Vink, Thomas de Bel, Bram van Ginneken, Jeroen van der Laak, Christina Hulsbergen-van de Kaa, and Geert Litjens · 2020
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Unsupervised learning of visual features by contrasting cluster assignments, 2020
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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A Simple Framework for Contrastive Learning of Visual Representations
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Big Self-Supervised Models are Strong Semi-Supervised Learners
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen A.W.M. Van Der Laak, Meyke Hermsen, Quirine F. Manson, Maschenka Balkenhol, Oscar Geessink, Nikolaos Stathonikos, Marcory C.R.F. Van Dijk, Peter Bult, Francisco Beca, Andrew H. Beck, Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Aoxiao Zhong, Qi Dou, Quanzheng Li, Hao Chen, Huang Jing Lin, Pheng Ann Heng, Christian Haß, Elia Bruni, Quincy Wong, Ugur Halici, Mustafa Ümit Öner, Rengul Cetin-Atalay, Matt Berseth, Vitali Khvatkov, Alexei Vylegzhanin, Oren Kraus, Muhammad Shaban, Nasir Rajpoot, Ruqayya Awan, Korsuk Sirinukunwattana, Talha Qaiser, Yee Wah Tsang, David Tellez, Jonas Annuscheit, Peter Hufnagl, Mira Valkonen, Kimmo Kartasalo, Leena Latonen, Pekka Ruusuvuori, Kaisa Liimatainen, Shadi Albarqouni, Bharti Mungal, Ami George, Stefanie Demirci, Nassir Navab, Seiryo Watanabe, Shigeto Seno, Yoichi Takenaka, Hideo Matsuda, Hady Ahmady Phoulady, Vassili Kovalev, Alexander Kalinovsky, Vitali Liauchuk, Gloria Bueno, M. Milagro Fernandez-Carrobles, Ismael Serrano, Oscar Deniz, Daniel Racoceanu, and Rui Venâncio
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Semi-supervised breast cancer histology classification using deep multiple instance learning and contrast predictive coding (Conference Presentation)
Ming Y. Lu, Richard J. Chen, and Faisal Mahmood · 2020
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Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels
Hans Pinckaers, Wouter Bulten, Jeroen van der Laak, and Geert Litjens · 2020
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Streaming convolutional neural networks for end-to-end learning with multi-megapixel images
Johannes Henricus Francisca Maria Pinckaers, Bram van Ginneken, and Geert Litjens · 2020
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He2rna: A deep learning model for transcriptomic learning from digital pathology, 2020
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Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides
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Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning
Korsuk Sirinukunwattana, Enric Domingo, Susan D. Richman, Keara L. Redmond, Andrew Blake, Clare Verrill, Simon J. Leedham, Aikaterini Chatzipli, Claire Hardy, Celina M. Whalley, Chieh Hsi Wu, Andrew D. Beggs, Ultan McDermott, Philip D. Dunne, Angela Meade, Steven M. Walker, Graeme I. Murray, Leslie Samuel, Matthew Seymour, Ian Tomlinson, Phil Quirke, Timothy Maughan, Jens Rittscher, and Viktor H. Koelzer · 2020
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Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala, Nicholas Hawkins, and Junzhou Huang · 2020
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