Bayes-MIL : A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images

Research output: Conference PapersRGC 32 - Refereed conference paper (without host publication)peer-review

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Detail(s)

Original languageEnglish
Number of pages11
Publication statusPublished - May 2023

Conference

Title11th International Conference on Learning Representations (ICLR 2023)
LocationHybrid
PlaceRwanda
CityKigali
Period1 - 5 May 2023

Abstract

Multiple instance learning (MIL) is a popular weakly-supervised learning model on the whole slide image (WSI) for AI-assisted pathology diagnosis. The recent advance in attention-based MIL allows the model to find its region-of-interest (ROI) for interpretation by learning the attention weights for image patches of WSI slides. However, we empirically find that the interpretability of some related methods is either untrustworthy as the principle of MIL is violated or unsatisfactory as the high-attention regions are not consistent with experts’ annotations. In this paper, we propose Bayes-MIL to address the problem from a probabilistic perspective. The induced patch-level uncertainty is proposed as a new measure of MIL interpretability, which outperforms previous methods in matching doctors annotations. We design a slide-dependent patch regularizer (SDPR) for the attention, imposing constraints derived from the MIL assumption, on the attention distribution. SDPR explicitly constrains the model to generate correct attention values. The spatial information is further encoded by an approximate convolutional conditional random field (CRF), for better interpretability. Experimental results show Bayes-MIL outperforms the related methods in patch-level and slide-level metrics and provides much better interpretable ROI on several large-scale WSI datasets.

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Citation Format(s)

Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images. / Cui, Yufei; Liu, Ziquan; Liu, Xiangyu et al.
2023. Paper presented at 11th International Conference on Learning Representations (ICLR 2023), Kigali, Rwanda.

Research output: Conference PapersRGC 32 - Refereed conference paper (without host publication)peer-review