Vim4Path: Self-Supervised Vision Mamba for Histopathology Images

arXiv:2404.13222v1 Announce Type: cross
Abstract: Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the Vision Mamba (Vim) architecture, …

arxiv cs.cv eess.iv images mamba type vision