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Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract discriminative feature representations within Whole Slide Images (WSIs) in computational pathology.
A new approach to linear filtering and prediction problems
Rudolph Emil Kalman · 1960
Earlier work this paper cites.
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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Multiple instance classification: Review, taxonomy and comparative study
Jaume Amores · 2013
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Multi-instance multi-label image classification: A neural approach
Zenghai Chen, Zheru Chi, Hong Fu, and Dagan Feng · 2013
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
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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Attention-based deep multiple instance learning
Maximilian Ilse, Jakub Tomczak, and Max Welling · 2018
Earlier work this paper cites.
Transpath: Transformer-based self-supervised learning for histopathological image classification
Xiyue Wang, Sen Yang, Jun Zhang, Minghui Wang, Jing Zhang, Junzhou Huang, Wei Yang, and Xiao Han · 2021
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.
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.
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.
Multimodal co-attention transformer for survival prediction in gigapixel whole slide images
Richard J Chen, Ming Y Lu, Wei-Hung Weng, Tiffany Y Chen, Drew FK Williamson, Trevor Manz, Maha Shady, and Faisal Mahmood · 2021
Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification
Hongrun Zhang, Yanda Meng, Yitian Zhao, Yihong Qiao, Xiaoyun Yang, Sarah E Coupland, and Yalin Zheng · 2022
Later among the works it cites.
Bracs: A dataset for breast carcinoma subtyping in h&e histology images
Nadia Brancati, Anna Maria Anniciello, Pushpak Pati, Daniel Riccio, Giosuè Scognamiglio, Guillaume Jaume, Giuseppe De Pietro, Maurizio Di Bonito, Antonio Foncubierta, Gerardo Botti, et al · 2022
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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
Later among the works it cites.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
Later among the works it cites.
Structured state space models for multiple instance learning in digital pathology
Leo Fillioux, Joseph Boyd, Maria Vakalopoulou, Paul-Henry Cournède, and Stergios Christodoulidis · 2023
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Cited alongside, same era.
Dt-mil: deformable transformer for multi-instance learning on histopathological image
Hang Li, Fan Yang, Yu Zhao, Xiaohan Xing, Jun Zhang, Mingxuan Gao, Junzhou Huang, Liansheng Wang, and Jianhua Yao · 2021
Cited alongside, same era.
Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2021
Cited alongside, same era.
Later among the works it cites.
Vision mamba: Efficient visual representation learning with bidirectional state space model
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang · 2024
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