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Whole slide image (WSI) assessment is a challenging and crucial step in cancer diagnosis and treatment planning.
Solving the multiple instance problem with axis-parallel rectangles
Thomas G. Dietterich, Richard H. Lathrop, and Tomás Lozano-Pérez · 1997
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Data efficient and weakly supervised computational pathology on whole slide images
Ming Y. Lu, Drew F.K. Williamson, Tiffany Y. Chen, Richard J. Chen, Matteo Barbieri, and Faisal Mahmood · 2004
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Data efficient and weakly supervised computational pathology on whole slide images
Ming Y. Lu, Drew F.K. Williamson, Tiffany Y. Chen, Richard J. Chen, Matteo Barbieri, and Faisal Mahmood · 2004
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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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Histopathological image analysis: A review
Metin N Gurcan, Senior Member, Laura E Boucheron, Ali Can, Anant Madabhushi, Nasir M Rajpoot, and Bulent Yener · 2009
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Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning
Bin Li, Yin Li, and Kevin W. Eliceiri · 2011
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Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning
Bin Li, Yin Li, and Kevin W. Eliceiri · 2011
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Multiple instance classification: Review, taxonomy and comparative study
Jaume Amores · 2013
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FitNets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 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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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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Toward a Shared Vision for Cancer Genomic Data
Robert L. Grossman, Allison P. Heath, Vincent Ferretti, Harold E. Varmus, Douglas R. Lowy, Warren A. Kibbe, and Louis M. Staudt · 2016
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Multiple instance learning: A survey of problem characteristics and applications
Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, and Ghyslain Gagnon · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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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, et al · 2017
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On Calibration of Modern Neural Networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer
A. Gordon Robertson, Jaegil Kim, Hikmat Al-Ahmadie, Joaquim Bellmunt, Guangwu Guo, Andrew D. Cherniack, Toshinori Hinoue, et al · 2017
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Attention-based deep multiple instance learning
Maximilian Ilse, Jakub M. Tomczak, and Max Welling · 2018
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Dynamic meta-embeddings for improved sentence representations
Douwe Kiela, Changhan Wang, and Kyunghyun Cho · 2018
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An integrated tcga pan-cancer clinical data resource to drive high-quality survival outcome analytics
Jianfang Liu, Tara Lichtenberg, Katherine A. Hoadley, Laila M. Poisson, Alexander J. Lazar, et al · 2018
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Deep multi-instance learning with dynamic pooling
Yongluan Yan, Xinggang Wang, Jiemin Fang, Wenyu Liu, Junzhou Huang, Jun Zhu, and Ichiro Takeuchi · 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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Multiple instance learning with graph neural networks
Ming Tu, Jing Huang, Xiaodong He, and Bowen Zhou · 2019
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Be your own teacher: Improve the performance of convolutional neural networks via self distillation
Linfeng Zhang, Jiebo Song, Anni Gao, Jingwei Chen, Chenglong Bao, and Kaisheng Ma · 2019
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Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira · 2021
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Transmil: Transformer based correlated multiple instance learning for whole slide image classification
Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, and Yongbing Zhang · 2021
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Molecular analysis of TCGA breast cancer histologic types
Aatish Thennavan, Francisco Beca, Youli Xia, Susana Garcia-Recio, Kimberly Allison, Laura C. Collins, Gary M. Tse, et al · 2021
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Self-Distillation: Towards Efficient and Compact Neural Networks
Linfeng Zhang, Chenglong Bao, and Kaisheng Ma · 2021
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Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
Richard J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan, and Faisal Mahmood · 2022
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On the Variance of the Adaptive Learning Rate and Beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han · 2019
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Be your own teacher: Improve the performance of convolutional neural networks via self distillation
Linfeng Zhang, Jiebo Song, Anni Gao, Jingwei Chen, Chenglong Bao, and Kaisheng Ma · 2019
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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
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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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A comparative study of u-net topologies for background removal in histopathology images
Abtin Riasatian, Maral Rasoolijaberi, Morteza Babaei, and H. R. Tizhoosh · 2020
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A comparative study of u-net topologies for background removal in histopathology images
Abtin Riasatian, Maral Rasoolijaberi, Morteza Babaei, and H. R. Tizhoosh · 2020
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Emerging properties in self-supervised vision transformers
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Perceiver IO: A general architecture for structured inputs & outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier J Henaff, Matthew Botvinick, Andrew Zisserman, Oriol Vinyals, and Joao Carreira · 2022
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Transformer based multiple instance learning for weakly supervised histopathology image segmentation
Ziniu Qian, Kailu Li, Maode Lai, Eric I.Chao Chang, Bingzheng Wei, Yubo Fan, and Yan Xu · 2022
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Learning representations with contrastive self-supervised learning for histopathology applications
Karin Stacke, Jonas Unger, Claes Lundström, and Gabriel Eilertsen · 2022
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Self-supervised learning in remote sensing: A review
Yi Wang, Conrad M. Albrecht, Nassim Ait Ali Braham, Lichao Mou, and Xiao Xiang Zhu · 2022
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Clustering-based multi-instance learning network for whole slide image classification
Wei Wu, Zhonghang Zhu, Baptiste Magnier, and Liansheng Wang · 2022
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Dual space multiple instance representative learning for medical image classification
Xiaoxian Zhang, Sheng Huang, Yi Zhang, Xiaohong Zhang, Mingchen Gao, and Liu Chen · 2022
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Image BERT pre-training with online tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong · 2022
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Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
Richard J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan, and Faisal Mahmood · 2022
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Iterative patch selection for high-resolution image recognition
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Pre-training segmentation models for histopathology
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DINOv2: Learning Robust Visual Features without Supervision
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Iterative patch selection for high-resolution image recognition
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Generalization of vision pre-trained models for histopathology
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