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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Published in:	IEEE transactions on circuits and systems for video technology Vol. 34; no. 10; pp. 9732 - 9744
Main Authors:	Qu, Linhao, Ma, Yingfan, Luo, Xiaoyuan, Guo, Qinhao, Wang, Manning, Song, Zhijian
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.10.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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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Abstract	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 .
AbstractList	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 .
Author	Song, Zhijian Luo, Xiaoyuan Guo, Qinhao Ma, Yingfan Qu, Linhao Wang, Manning
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References	ref13 ref12 ref15 ref52 ref55 ref10 ref17 ref16 ref19 ref51 ref50 ref46 ref45 ref48 Yang (ref53) 2023 ref47 ref42 ref41 ref44 ref43 Lu (ref11) 2019 ref49 ref8 Shao (ref24); 34 ref9 Qu (ref14); 35 ref4 ref3 ref6 ref5 Qu (ref7); 36 ref40 ref35 ref34 Chen (ref38) 2022 ref37 ref36 ref30 ref33 ref32 ref2 Ilse (ref18) ref1 ref39 Wang (ref54) 2022 Yan (ref28) Maron (ref31); 10 ref23 ref26 ref25 ref20 ref22 Wang (ref27); 35 ref21 ref29
References_xml	– ident: ref33 doi: 10.1109/TMI.2022.3227066 – ident: ref1 doi: 10.1088/1361-6560/ac910a – ident: ref6 doi: 10.1109/TCSVT.2023.3294938 – start-page: 2127 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref18 article-title: Attention-based deep multiple instance learning – ident: ref26 doi: 10.1007/978-3-030-87237-3_32 – ident: ref34 doi: 10.1109/TMI.2022.3176598 – ident: ref15 doi: 10.1038/s41591-019-0508-1 – volume: 35 start-page: 18009 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref27 article-title: SCL-WC: Cross-slide contrastive learning for weakly-supervised whole-slide image classification – ident: ref41 doi: 10.1109/CVPR42600.2020.00975 – ident: ref22 doi: 10.1109/CVPR46437.2021.01409 – ident: ref13 doi: 10.1007/978-3-031-16434-7_3 – ident: ref37 doi: 10.1109/CVPR52688.2022.01567 – ident: ref8 doi: 10.59275/j.melba.2023-5g54 – ident: ref36 doi: 10.1109/TMI.2023.3241204 – ident: ref35 doi: 10.1109/tmi.2023.3253760 – volume: 10 start-page: 1 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref31 article-title: A framework for multiple-instance learning – ident: ref10 doi: 10.1109/TMI.2020.3021387 – ident: ref3 doi: 10.1038/s41586-021-03512-4 – volume: 34 start-page: 2136 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref24 article-title: TransMIL: Transformer based correlated multiple instance learning for whole slide image classification – start-page: 662 volume-title: Proc. Asian Conf. Mach. Learn. (ACML) ident: ref28 article-title: Deep multi-instance learning with dynamic pooling – year: 2022 ident: ref38 article-title: Self-supervised vision transformers learn visual concepts in histopathology publication-title: arXiv:2203.00585 – ident: ref51 doi: 10.1007/978-3-031-26387-3_26 – ident: ref21 doi: 10.1609/aaai.v34i04.6030 – ident: ref48 doi: 10.1109/TCSVT.2021.3128054 – ident: ref52 doi: 10.1016/j.media.2023.102748 – ident: ref30 doi: 10.1093/bioinformatics/btw252 – ident: ref39 doi: 10.1002/path.6027 – ident: ref17 doi: 10.1038/s41598-020-66333-x – ident: ref25 doi: 10.1109/ICCV48922.2021.00398 – ident: ref44 doi: 10.1109/TCSVT.2022.3219893 – ident: ref46 doi: 10.1109/TCSVT.2022.3141051 – year: 2023 ident: ref53 article-title: TPMIL: Trainable prototype enhanced multiple instance learning for whole slide image classification publication-title: arXiv:2305.00696 – ident: ref45 doi: 10.1109/TCSVT.2022.3169469 – year: 2019 ident: ref11 article-title: Semi-supervised histology classification using deep multiple instance learning and contrastive predictive coding publication-title: arXiv:1910.10825 – ident: ref29 doi: 10.1016/j.patcog.2017.08.026 – ident: ref23 doi: 10.1109/CVPR52688.2022.01824 – year: 2022 ident: ref54 article-title: PiCO+: Contrastive label disambiguation for robust partial label learning publication-title: arXiv:2201.08984 – ident: ref4 doi: 10.1109/TMI.2019.2927182 – ident: ref20 doi: 10.1016/j.media.2020.101789 – ident: ref19 doi: 10.1109/CVPR42600.2020.00391 – volume: 36 start-page: 1 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref7 article-title: The rise of ai language pathologists: Exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification – ident: ref50 doi: 10.1109/TCSVT.2023.3281151 – ident: ref32 doi: 10.1016/j.modpat.2023.100316 – ident: ref47 doi: 10.1109/TCSVT.2020.2992276 – ident: ref9 doi: 10.1016/j.media.2019.03.009 – ident: ref16 doi: 10.1007/978-3-030-59722-1_50 – ident: ref42 doi: 10.1109/ICCV48922.2021.00951 – ident: ref49 doi: 10.1109/TCSVT.2022.3209209 – ident: ref12 doi: 10.1038/s41551-020-00682-w – ident: ref55 doi: 10.1109/CVPR.2009.5206848 – ident: ref40 doi: 10.5555/3524938.3525087 – volume: 35 start-page: 15368 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref14 article-title: Bi-directional weakly supervised knowledge distillation for whole slide image classification – ident: ref5 doi: 10.1016/j.media.2021.102298 – ident: ref2 doi: 10.1016/j.media.2020.101813 – ident: ref43 doi: 10.1109/TCSVT.2022.3207174
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SubjectTerms	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
Title	Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier Is All You Need
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