  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, inspired by state space models, within the DINO
framework for representation learning in computational pathology. We evaluate
the performance of Vim against Vision Transformers (ViT) on the Camelyon16
dataset for both patch-level and slide-level classification. Our findings
highlight Vim's enhanced performance compared to ViT, particularly at smaller
scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar
size. An explainability analysis further highlights Vim's capabilities, which
reveals that Vim uniquely emulates the pathologist workflow-unlike ViT. This
alignment with human expert analysis highlights Vim's potential in practical
diagnostic settings and contributes significantly to developing effective
representation-learning algorithms in computational pathology. We release the
codes and pretrained weights at
\url{https://github.com/AtlasAnalyticsLab/Vim4Path}.
