Vision Foundation Models in Medical Image Analysis: Advances and Challenges

TL;DR

This paper reviews recent advances in Vision Foundation Models like ViT and SAM for medical image segmentation, highlighting challenges, solutions, and future directions to improve clinical applications.

Contribution

It provides a comprehensive overview of how VFMs are adapted to medical imaging, focusing on domain adaptation, model compression, and federated learning techniques.

Findings

VFMs show strong potential for medical image segmentation.

Challenges include domain differences and limited datasets.

Emerging methods like federated learning and knowledge distillation are promising.

Abstract

The rapid development of Vision Foundation Models (VFMs), particularly Vision Transformers (ViT) and Segment Anything Model (SAM), has sparked significant advances in the field of medical image analysis. These models have demonstrated exceptional capabilities in capturing long-range dependencies and achieving high generalization in segmentation tasks. However, adapting these large models to medical image analysis presents several challenges, including domain differences between medical and natural images, the need for efficient model adaptation strategies, and the limitations of small-scale medical datasets. This paper reviews the state-of-the-art research on the adaptation of VFMs to medical image segmentation, focusing on the challenges of domain adaptation, model compression, and federated learning. We discuss the latest developments in adapter-based improvements, knowledge distillation techniques, and multi-scale contextual feature modeling, and propose future directions to overcome these bottlenecks. Our analysis highlights the potential of VFMs, along with emerging methodologies such as federated learning and model compression, to revolutionize medical image analysis and enhance clinical applications. The goal of this work is to provide a comprehensive overview of current approaches and suggest key areas for future research that can drive the next wave of innovation in medical image segmentation.