Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher

被引：0
|
作者：
Wang, Hongyi [3 ]
Luo, Luyang [1 ]
Wang, Fang [2 ]
Tong, Ruofeng [3 ,4 ]
Chen, Yen-Wei [3 ,5 ]
Hu, Hongjie [2 ]
Lin, Lanfen [3 ]
Chen, Hao [6 ]
机构：
[1] The Hong Kong University of Science and Technology, Department of Computer Science and Engineering, Hong Kong, Hong Kong
[2] Sir Run Run Shaw Hospital, Department of Radiology, Hangzhou,310016, China
[3] Zhejiang University, College of Computer Science and Technology, Hangzhou,310063, China
[4] Zhejiang Laboratory, Research Center for Healthcare Data Science, Hangzhou,311121, China
[5] Ritsumeikan University, College of Information Science and Engineering, Osaka, Ibaraki,567-8570, Japan
[6] The Hong Kong University of Science and Technology, Division of Life Science, Department of Computer Science and Engineering, Department of Chemical and Biological Engineering, Hong Kong, Hong Kong
来源：
IEEE Transactions on Medical Imaging | 2024年 / 43卷 / 11期
关键词：
Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However; a major challenge persists due to the high computational cost associated with processing these gigapixel images. Existing methods generally adopt a two-stage approach; comprising a non-learnable feature embedding stage and a classifier training stage. Though it can greatly reduce memory consumption by using a fixed feature embedder pre-trained on other domains; such a scheme also results in a disparity between the two stages; leading to suboptimal classification accuracy. To address this issue; we propose that a bag-level classifier can be a good instance-level teacher. Based on this idea; we design Iteratively Coupled Multiple Instance Learning (ICMIL) to couple the embedder and the bag classifier at a low cost. ICMIL initially fixes the patch embedder to train the bag classifier; followed by fixing the bag classifier to fine-tune the patch embedder. The refined embedder can then generate better representations in return; leading to a more accurate classifier for the next iteration. To realize more flexible and more effective embedder fine-tuning; we also introduce a teacher-student framework to efficiently distill the category knowledge in the bag classifier to help the instance-level embedder fine-tuning. Intensive experiments were conducted on four distinct datasets to validate the effectiveness of ICMIL. The experimental results consistently demonstrated that our method significantly improves the performance of existing MIL backbones; achieving state-of-the-art results. The code and the organized datasets can be accessed by: https://github.com/Dootmaan/ICMIL/tree/confidence-based. © 1982-2012 IEEE;
D O I：
10.1109/TMI.2024.3404549
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页码：3964 / 3976
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