Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier Is All You Need

Weakly supervised whole slide image classification is usually formulated as a multiple instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut out of it are treated as instances. Existing methods either train an instance classifier through pseudo-labeling or aggreg...

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Vydané v:IEEE transactions on circuits and systems for video technology Ročník 34; číslo 10; s. 9732 - 9744
Hlavní autori: Qu, Linhao, Ma, Yingfan, Luo, Xiaoyuan, Guo, Qinhao, Wang, Manning, Song, Zhijian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Aggregates
Algorithms
Attention
Circuits and systems
Classification
Contrastive learning
Feature extraction
Image classification
Labels
Machine learning
Multiple instance learning
Noise measurement
prototype learning
Prototypes
Training
whole slide image classification
ISSN:1051-8215, 1558-2205
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Shrnutí:Weakly supervised whole slide image classification is usually formulated as a multiple instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut out of it are treated as instances. Existing methods either train an instance classifier through pseudo-labeling or aggregate instance features into a bag feature through attention mechanisms and then train a bag classifier, where the attention scores can be used for instance-level classification. However, the pseudo instance labels constructed by the former usually contain a lot of noise, and the attention scores constructed by the latter are not accurate enough, both of which affect their performance. In this paper, we propose an instance-level MIL framework based on contrastive learning and prototype learning to effectively accomplish both instance classification and bag classification tasks. To this end, we propose an instance-level weakly supervised contrastive learning algorithm for the first time under the MIL setting to effectively learn instance feature representation. We also propose an accurate pseudo label generation method through prototype learning. We then develop a joint training strategy for weakly supervised contrastive learning, prototype learning, and instance classifier training. Extensive experiments and visualizations on four datasets demonstrate the powerful performance of our method. Codes are available at https://github.com/miccaiif/INS .
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2024.3400876