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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Vydáno v:	IEEE transactions on circuits and systems for video technology Ročník 34; číslo 10; s. 9732 - 9744
Hlavní autoři:	Qu, Linhao, Ma, Yingfan, Luo, Xiaoyuan, Guo, Qinhao, Wang, Manning, Song, Zhijian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.10.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
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