SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images

• URL: http://arxiv.org/abs/2505.06710v1
• Date: Sat, 10 May 2025 17:23:36 GMT
• Title: SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images
• Authors: Yicheng Song, Tiancheng Lin, Die Peng, Su Yang, Yi Xu,
• Abstract summary: This paper proposes to pre-train feature extractor for MIL via a weakly-supervised scheme.<n>To learn effective features for MIL, we delve into several key components, including strong data augmentation, a non-linear prediction head and the robust loss function.<n>We conduct experiments on common large-scale WSI datasets and find it achieves better performance than other pre-training schemes.
• Score: 12.827931905880163
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Various multi-instance learning (MIL) based approaches have been developed and successfully applied to whole-slide pathological images (WSI). Existing MIL methods emphasize the importance of feature aggregators, but largely neglect the instance-level representation learning. They assume that the availability of a pre-trained feature extractor can be directly utilized or fine-tuned, which is not always the case. This paper proposes to pre-train feature extractor for MIL via a weakly-supervised scheme, i.e., propagating the weak bag-level labels to the corresponding instances for supervised learning. To learn effective features for MIL, we further delve into several key components, including strong data augmentation, a non-linear prediction head and the robust loss function. We conduct experiments on common large-scale WSI datasets and find it achieves better performance than other pre-training schemes (e.g., ImageNet pre-training and self-supervised learning) in different downstream tasks. We further show the compatibility and scalability of the proposed scheme by deploying it in fine-tuning the pathological-specific models and pre-training on merged multiple datasets. To our knowledge, this is the first work focusing on the representation learning for MIL.

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