Report
Vim4Path: Self-Supervised Vision Mamba for Histopathology Images
| العنوان: | Vim4Path: Self-Supervised Vision Mamba for Histopathology Images |
|---|---|
| المؤلفون: | Nasiri-Sarvi, Ali, Trinh, Vincent Quoc-Huy, Rivaz, Hassan, Hosseini, Mahdi S. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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}. Comment: Accepted in CVPR2024 (9th Workshop on Computer Vision for Microscopy Image Analysis) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.13222 |
| رقم الانضمام: | edsarx.2404.13222 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |