# Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification Source: [Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:19:03.690Z Authors: Bo Zhang, Xu Xinan, Shuo Yan, Yu Bai, Zheng Zhang, Wufan Wang, Wendong Wang Published: 2025-08-05T04:54:49.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()