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Histopathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment.
Systematic analysis of breast cancer morphology uncovers stromal features associated with survival
Andrew H Beck, Ankur R Sangoi, Samuel Leung, Robert J Marinelli, Torsten O Nielsen, Marc J van de Vijver, Robert B West, Matt van de Rijn, and Daphne Koller · 1946
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Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling
Yinyin Yuan, Henrik Failmezger, Oscar M Rueda, H Raza Ali, Stefan Gräf, Suet-Feung Chin, Roland F Schwarz, Christina Curtis, Mark J Dunning, Helen Bardwell, Nicola Johnson, Sarah Doyle, Gulisa Turashvili, Elena Provenzano, Sam Aparicio, Carlos Caldas, and Florian Markowetz · 1946
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Sur la distance de deux lois de probabilité
Maurice Fréchet · 1957
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The stanford tissue microarray database
Robert J. Marinelli, Kelli Montgomery, Chih Long Liu, Nigam Shah, Wijan Prapong, Michael Nitzberg, Zachariah K Zachariah, Gavin Sherlock, Yasodha Natkunam, Robert B West, Matt van de Rijn, Patrick O Brown, and Catherine A Ball · 2008
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks, 2015
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Stacked sparse autoencoder (ssae) for nuclei detection on breast cancer histopathology images
Jun Xu, Lei Xiang, Qingshan Liu, Hannah Gilmore, Jianzhong Wu, Jinghai Tang, and Anant Madabhushi · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Revisiting classifier two-sample tests, 2016
David Lopez-Paz and Maxime Oquab · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Geometric GAN, 2017
Jae Hyun Lim and Jong Chul Ye · 2017
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Shane Barratt and Rishi Sharma · 2018
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Demystifying MMD GANs
Mikołaj Bińkowski, Dougal J. Sutherland, Michael Arbel, and Arthur Gretton · 2018
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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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Automatic segmentation of histopathological slides of renal tissue using deep learning
Thomas de Bel, Meyke Hermsen, Bart Smeets, Luuk Hilbrands, Jeroen van der Laak, and Geert Litjens · 2018
Cited alongside, same era.
Michael Gadermayr, Laxmi Gupta, Barbara M Klinkhammer, Peter Boor, and Dorit Merhof · 2018
Cited alongside, same era.
Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images
Le Hou, Vu Nguyen, Ariel B Kanevsky, Dimitris Samaras, Tahsin M Kurc, Tianhao Zhao, Rajarsi R Gupta, Yi Gao, Wenjin Chen, David Foran, and Joel H Saltz · 2018
Cited alongside, same era.
Unsupervised learning for cell-level visual representation in histopathology images with generative adversarial networks
Bo Hu, Ye Tang, Eric I-Chao Chang, Yubo Fan, Maode Lai, and Yan Xu · 2018
Cited alongside, same era.
An empirical study on evaluation metrics of generative adversarial networks, 2018
Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features
Talha Qaiser, Yee-Wah Tsang, Daiki Taniyama, Naoya Sakamoto, Kazuaki Nakane, David Epstein, and Nasir Rajpoot · 2019
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Joint segmentation and fine-grained classification of nuclei in histopathology images
Hui Qu, Gregory Riedlinger, Pengxiang Wu, Qiaoying Huang, Jingru Yi, Subhajyoti De, and Dimitris Metaxas · 2019
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High-definition spatial transcriptomics for in situ tissue profiling
Sanja Vickovic, Gökcen Eraslan, Fredrik Salmén, and et al · 2019
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Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks
Jason W. Wei, Laura J. Tafe, Yevgeniy A. Linnik, Louis J. Vaickus, Naofumi Tomita, and Saeed Hassanpour · 2019
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Look, investigate, and classify: A deep hybrid attention method for breast cancer classification
Bolei Xu, Jingxin Liu, Xianxu Hou, Bozhi Liu, Jon Garibaldi, Ian O Ellis, Andy Green, Linlin Shen, and Guoping Qiu · 2019
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Gao Huang, Yang Yuan, Qiantong Xu, Chuan Guo, Yu Sun, Felix Wu, and Kilian Weinberger · 2018
Cited alongside, same era.
100,000 histological images of human colorectal cancer and healthy tissue, April 2018
Jakob Nikolas Kather, Niels Halama, and Alexander Marx · 2018
Cited alongside, same era.
Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network
Jared L. Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger · 2018
Cited alongside, same era.
Deephit: A deep learning approach to survival analysis with competing risks
C. Lee, W. Zame, Jinsung Yoon, and M. V. D. Schaar · 2018
Cited alongside, same era.
Deep adversarial training for multi-organ nuclei segmentation in histopathology images, 09 2018
Faisal Mahmood, Daniel Borders, Richard Chen, Gregory McKay, Kevan J Salimian, Alexander Baras, and Nicholas Durr · 2018
Cited alongside, same era.
UMAP: Uniform Manifold Approximation and Projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Computational histological staining and destaining of prostate core biopsy RGB images with Generative Adversarial Neural Networks
Aman Rana, Gregory Yauney, Alarice Lowe, and Pratik Shah · 2018
Cited alongside, same era.
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Super-resolved spatial transcriptomics by deep data fusion
Ludvig Bergenstråhle, Bryan He, Joseph Bergenstråhle, Alma Andersson, Joakim Lundeberg, James Zou, and Jonas Maaskola · 2020
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Pan-cancer analysis of whole genomes
Peter J. Campbell, Gad Getz, Jan O. Korbel, and et al · 2020
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Deep learning links histology, molecular signatures and prognosis in cancer
Nicolas Coudray and Aristotelis Tsirigos · 2020
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Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes
Stefan C. Dentro, Ignaty Leshchiner, and et al · 2020
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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
Yu Fu, Alexander W. Jung, Ramon Viñas Torne, Santiago Gonzalez, Harald Vöhringer, Artem Shmatko, Lucy R. Yates, Mercedes Jimenez-Linan, Luiza Moore, and Moritz Gerstung · 2020
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The evolutionary history of 2,658 cancers
Moritz Gerstung, Clemency Jolly, and et al · 2020
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Integrating spatial gene expression and breast tumour morphology via deep learning
Bryan He, Ludvig Bergenstråhle, Linnea Stenbeck, Abubakar Abid, Alma Andersson, Åke Borg, Jonas Maaskola, Joakim Lundeberg, and James Zou · 2020
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Pan-cancer image-based detection of clinically actionable genetic alterations
Jakob Nikolas Kather, Lara R. Heij, Heike I. Grabsch, and et al · 2020
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Synthesis of diagnostic quality cancer pathology images by generative adversarial networks
Adrian B Levine, Jason Peng, David Farnell, Mitchell Nursey, and et al · 2020
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Learning a low dimensional manifold of real cancer tissue with pathologygan, 2020
Adalberto Claudio Quiros, Roderick Murray-Smith, and Ke YuCoudan · 2020
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A deep learning model to predict rna-seq expression of tumours from whole slide images
Benoît Schmauch, Alberto Romagnoni, Elodie Pronier, Charlie Saillard, Pascale Maillé, Julien Calderaro, Aurélie Kamoun, Meriem Sefta, Sylvain Toldo, Mikhail Zaslavskiy, Thomas Clozel, Matahi Moarii, Pierre Courtiol, and Gilles Wainrib · 2020
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Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities
Yuliang Wang, Shuyi Ma, and Walter L. Ruzzo · 2020
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Deep learning predicts molecular subtype of muscle-invasive bladder cancer from conventional histopathological slides
Ann-Christin Woerl, Markus Eckstein, Josephine Geiger, Daniel C. Wagner, Tamas Daher, Philipp Stenzel, Aurélie Fernandez, Arndt Hartmann, Michael Wand, Wilfried Roth, and Sebastian Foersch · 2020
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