SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images
Journal:
arXiv
Published Date:
May 10, 2025
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.
