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Whole slide imaging is fundamental to biomedical microscopy and computational pathology.
Solving the multiple instance problem with axis-parallel rectangles
Thomas G Dietterich, Richard H Lathrop, and Tomás Lozano-Pérez · 1997
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Label-free biomedical imaging with high sensitivity by stimulated raman scattering microscopy
Christian W. Freudiger, Wei Min, Brian G. Saar, Sijia Lu, Gary R. Holtom, Chengwei He, Jason C. Tsai, Jing X. Kang, and X. Sunney Xie · 2008
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A method for normalizing histology slides for quantitative analysis
Marc Macenko, Marc Niethammer, James S Marron, David Borland, John T Woosley, Xiaojun Guan, Charles Schmitt, and Nancy E Thomas · 2009
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Patch-based convolutional neural network for whole slide tissue image classification
Le Hou, Dimitris Samaras, Tahsin M Kurc, Yi Gao, James E Davis, and Joel H Saltz · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Wsisa: Making survival prediction from whole slide histopathological images
Xinliang Zhu, Jiawen Yao, Feiyun Zhu, and Junzhou Huang · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated raman scattering microscopy
Daniel A Orringer, Balaji Pandian, Yashar S Niknafs, Todd C Hollon, Julianne Boyle, Spencer Lewis, Mia Garrard, Shawn L Hervey-Jumper, Hugh JL Garton, Cormac O Maher, et al · 2017
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Attention-based deep multiple instance learning
Maximilian Ilse, Jakub Tomczak, and Max Welling · 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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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 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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Deep multi-instance learning for survival prediction from whole slide images
Jiawen Yao, Xinliang Zhu, and Junzhou Huang · 2019
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Patch transformer for multi-tagging whole slide histopathology images
Weijian Li, Viet-Duy Nguyen, Haofu Liao, Matt Wilder, Ke Cheng, and Jiebo Luo · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Near real-time intraoperative brain tumor diagnosis using stimulated raman histology and deep neural networks
Todd C Hollon, Balaji Pandian, Arjun R Adapa, Esteban Urias, Akshay V Save, Siri Sahib S Khalsa, Daniel G Eichberg, Randy S D’Amico, Zia U Farooq, Spencer Lewis, et al · 2020
Cited alongside, same era.
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
Cited alongside, same era.
A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
Cited alongside, same era.
Pan-cancer integrative histology-genomic analysis via multimodal deep learning
Richard J Chen, Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Jana Lipkova, Zahra Noor, Muhammad Shaban, Maha Shady, Mane Williams, Bumjin Joo, et al · 2022
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Fast and scalable search of whole-slide images via self-supervised deep learning
Chengkuan Chen, Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Andrew J Schaumberg, and Faisal Mahmood · 2022
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Transformer-based unsupervised contrastive learning for histopathological image classification
Xiyue Wang, Sen Yang, Jun Zhang, Minghui Wang, Jing Zhang, Wei Yang, Junzhou Huang, and Xiao Han · 2022
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Scl-wc: Cross-slide contrastive learning for weakly-supervised whole-slide image classification
Xiyue Wang, Jinxi Xiang, Jun Zhang, Sen Yang, Zhongyi Yang, Ming-Hui Wang, Jing Zhang, Wei Yang, Junzhou Huang, and Xiao Han · 2022
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Contrastive multiple instance learning: An unsupervised framework for learning slide-level representations of whole slide histopathology images without labels
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Data-efficient and weakly supervised computational pathology on whole-slide images
Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Matteo Barbieri, and Faisal Mahmood · 2021
Cited alongside, same era.
Transmil: Transformer based correlated multiple instance learning for whole slide image classification
Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, et al · 2021
Cited alongside, same era.
Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning
Bin Li, Yin Li, and Kevin W Eliceiri · 2021
Cited alongside, same era.
Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
Cited alongside, same era.
Ai-based pathology predicts origins for cancers of unknown primary
Ming Y Lu, Tiffany Y Chen, Drew FK Williamson, Melissa Zhao, Maha Shady, Jana Lipkova, and Faisal Mahmood · 2021
Cited alongside, same era.
Thomas E Tavolara, Metin N Gurcan, and M Khalid Khan Niazi · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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OpenSRH: optimizing brain tumor surgery using intraoperative stimulated raman histology
Cheng Jiang, Asadur Zaman Chowdury, Xinhai Hou, Akhil Kondepudi, Christian Freudiger, Kyle Stephen Conway, Sandra Camelo-Piragua, Daniel A Orringer, Honglak Lee, and Todd Hollon · 2022
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the digital brain tumour atlas, an open histopathology resource
Thomas Roetzer-Pejrimovsky, Anna-Christina Moser, Baran Atli, Clemens Christian Vogel, Petra A Mercea, Romana Prihoda, Ellen Gelpi, Christine Haberler, Romana Höftberger, Johannes A Hainfellner, et al · 2022
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Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging
Todd Hollon, Cheng Jiang, Asadur Chowdury, Mustafa Nasir-Moin, Akhil Kondepudi, Alexander Aabedi, Arjun Adapa, Wajd Al-Holou, Jason Heth, Oren Sagher, et al · 2023
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Hierarchical discriminative learning improves visual representations of biomedical microscopy
Cheng Jiang, Xinhai Hou, Akhil Kondepudi, Asadur Chowdury, Christian W Freudiger, Daniel A Orringer, Honglak Lee, and Todd C Hollon · 2023
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A visual–language foundation model for pathology image analysis using medical twitter
Zhi Huang, Federico Bianchi, Mert Yuksekgonul, Thomas J Montine, and James Zou · 2023
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Giga-ssl: Self-supervised learning for gigapixel images
Tristan Lazard, Marvin Lerousseau, Etienne Decencière, and Thomas Walter · 2023
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Local-to-global spatial learning for whole-slide image representation and classification
Jiahui Yu, Tianyu Ma, Yu Fu, Hang Chen, Maode Lai, Cheng Zhuo, and Yingke Xu · 2023
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Interventional bag multi-instance learning on whole-slide pathological images
Tiancheng Lin, Zhimiao Yu, Hongyu Hu, Yi Xu, and Chang-Wen Chen · 2023
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Visual language pretrained multiple instance zero-shot transfer for histopathology images
Ming Y Lu, Bowen Chen, Andrew Zhang, Drew FK Williamson, Richard J Chen, Tong Ding, Long Phi Le, Yung-Sung Chuang, and Faisal Mahmood · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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A cookbook of self-supervised learning
Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Gregoire Mialon, Yuandong Tian, et al · 2023
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Towards a general-purpose foundation model for computational pathology
Richard J Chen, Tong Ding, Ming Y Lu, Drew FK Williamson, Guillaume Jaume, Andrew H Song, Bowen Chen, Andrew Zhang, Daniel Shao, Muhammad Shaban, et al · 2024
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To compress or not to Compress-Self-Supervised learning and information theory: A review
Ravid Shwartz Ziv and Yann LeCun · 2024
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