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Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis.
Digital pathology image analysis: opportunities and challenges
Anant Madabhushi · 2009
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Histology image analysis for carcinoma detection and grading
Lei He, L Rodney Long, Sameer Antani, and George R Thoma · 2012
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, 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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Attention-aware deep reinforcement learning for video face recognition
Yongming Rao, Jiwen Lu, and J. Zhou · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, undefinedukasz Kaiser, and Illia Polosukhin · 2017
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Revisiting multiple instance neural networks
Xinggang Wang, Yongluan Yan, Peng Tang, Xiang Bai, and Wenyu Liu · 2018
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Attention-based deep multiple instance learning
Maximilian Ilse, Jakub M. Tomczak, and M. Welling · 2018
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Squeeze-and-excitation networks
Jie Hu, L. Shen, and G. Sun · 2018
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Cbam: Convolutional block attention module
S. Woo, Jongchan Park, Joon-Young Lee, and In-So Kweon · 2018
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Attention-based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides
Naofumi Tomita, Behnaz Abdollahi, Jason Wei, Bing Ren, A. Suriawinata, and S. Hassanpour · 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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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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Camel: A weakly supervised learning framework for histopathology image segmentation
G. Xu, Zhigang Song, Zhuo Sun, Calvin Ku, Z. Yang, C. Liu, S. Wang, Jianpeng Ma, and W. Xu · 2019
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Weakly supervised deep learning for whole slide lung cancer image analysis
Xi Wang, Hao Chen, Caixia Gan, Huangjing Lin, Qi Dou, Efstratios Tsougenis, Qitao Huang, Muyan Cai, and Pheng-Ann Heng · 2019
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Lookahead optimizer: k steps forward, 1 step back
Michael Ruogu Zhang, James Lucas, Geoffrey E. Hinton, and Jimmy Ba · 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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A comprehensive review for breast histopathology image analysis using classical and deep neural networks
Xiaomin Zhou, Chen Li, Md Mamunur Rahaman, Yudong Yao, Shiliang Ai, Changhao Sun, Qian Wang, Yong Zhang, Mo Li, Xiaoyan Li, et al · 2020
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Multiple instance learning with center embeddings for histopathology classification
P. Chikontwe, Meejeong Kim, S. Nam, H. Go, and S. Park · 2020
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Beyond classification: Whole slide tissue histopathology analysis by end-to-end part learning
Chensu Xie, Hassan Muhammad, Chad M Vanderbilt, Raul Caso, Dig Vijay Kumar Yarlagadda, Gabriele Campanella, and Thomas J Fuchs · 2020
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Learning joint spatial-temporal transformations for video inpainting
Yanhong Zeng, Jianlong Fu, and Hongyang Chao · 2020
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How much position information do convolutional neural networks encode?
Md Amirul Islam, Sen Jia, and Neil D. B. Bruce · 2020
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Renal cell carcinoma detection and subtyping with minimal point-based annotation in whole-slide images
Zeyu Gao, Pargorn Puttapirat, Jiangbo Shi, and Chen Li · 2020
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Deep neural network models for computational histopathology: A survey
Chetan L Srinidhi, Ozan Ciga, and Anne L Martel · 2020
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Weakly-supervised learning for lung carcinoma classification using deep learning
Fahdi Kanavati, Gouji Toyokawa, Seiya Momosaki, Michael Rambeau, Yuka Kozuma, Fumihiro Shoji, Koji Yamazaki, Sadanori Takeo, Osamu Iizuka, and Masayuki Tsuneki · 2020
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Multi-scale domain-adversarial multiple-instance cnn for cancer subtype classification with unannotated histopathological images
Noriaki Hashimoto, Daisuke Fukushima, Ryoichi Koga, Yusuke Takagi, Kaho Ko, Kei Kohno, Masato Nakaguro, Shigeo Nakamura, Hidekata Hontani, and Ichiro Takeuchi · 2020
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Deep learning-enabled breast cancer hormonal receptor status determination from base-level h&e stains
Nikhil Naik, Ali Madani, Andre Esteva, Nitish Shirish Keskar, Michael F Press, Daniel Ruderman, David B Agus, and Richard Socher · 2020
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Pre-trained image processing transformer
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2020
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Learning texture transformer network for image super-resolution
Fuzhi Yang, Huan Yang, J. Fu, Hongtao Lu, and B. Guo · 2020
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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
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Transunet: Transformers make strong encoders for medical image segmentation
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Transfuse: Fusing transformers and cnns for medical image segmentation
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Nyströmformer: A nyström-based algorithm for approximating self-attention
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A. Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, M. Dehghani, Matthias Minderer, G. Heigold, S. Gelly, Jakob Uszkoreit, and N. Houlsby · 2021
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Conditional positional encodings for vision transformers
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