Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification

• URL: http://arxiv.org/abs/2508.03081v1
• Date: Tue, 05 Aug 2025 04:54:49 GMT
• Title: Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification
• Authors: Bo Zhang, Xu Xinan, Shuo Yan, Yu Bai, Zheng Zhang, Wufan Wang, Wendong Wang,
• Abstract summary: We propose Contrastive Cross-Bag Augmentation ($C2Aug$) to sample instances from all bags with the same class to increase the diversity of pseudo-bags.<n>New instances into the pseudo-bag increases the number of critical instances (e.g., tumor instances)<n>This increase results in a reduced occurrence of pseudo-bags containing few critical instances, thereby limiting model performance.<n>We introduce a bag-level and group-level contrastive learning framework to enhance the discrimination of features with distinct semantic meanings.
• Score: 22.715117957704052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent pseudo-bag augmentation methods for Multiple Instance Learning (MIL)-based Whole Slide Image (WSI) classification sample instances from a limited number of bags, resulting in constrained diversity. To address this issue, we propose Contrastive Cross-Bag Augmentation ($C^2Aug$) to sample instances from all bags with the same class to increase the diversity of pseudo-bags. However, introducing new instances into the pseudo-bag increases the number of critical instances (e.g., tumor instances). This increase results in a reduced occurrence of pseudo-bags containing few critical instances, thereby limiting model performance, particularly on test slides with small tumor areas. To address this, we introduce a bag-level and group-level contrastive learning framework to enhance the discrimination of features with distinct semantic meanings, thereby improving model performance. Experimental results demonstrate that $C^2Aug$ consistently outperforms state-of-the-art approaches across multiple evaluation metrics.

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