Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?

TL;DR

This study evaluates the ability of vision foundation models, enhanced with novel fine-tuning techniques and a new decoder head, to improve domain generalization in medical image segmentation across diverse datasets and clinical settings.

Contribution

It introduces a new decode head architecture, HQHSAM, and systematically assesses the domain generalization capabilities of various foundation models with different fine-tuning methods in medical segmentation.

Findings

FMs with HQHSAM improve segmentation across domains.

PEFT techniques' effectiveness varies among models.

FMs show promise for robust medical image segmentation.

Abstract

Neural networks achieve state-of-the-art performance in many supervised learning tasks when the training data distribution matches the test data distribution. However, their performance drops significantly under domain (covariate) shift, a prevalent issue in medical image segmentation due to varying acquisition settings across different scanner models and protocols. Recently, foundational models (FMs) trained on large datasets have gained attention for their ability to be adapted for downstream tasks and achieve state-of-the-art performance with excellent generalization capabilities on natural images. However, their effectiveness in medical image segmentation remains underexplored. In this paper, we investigate the domain generalization performance of various FMs, including DinoV2, SAM, MedSAM, and MAE, when fine-tuned using various parameter-efficient fine-tuning (PEFT) techniques such as Ladder and Rein (+LoRA) and decoder heads. We introduce a novel decode head architecture, HQHSAM, which simply integrates elements from two state-of-the-art decoder heads, HSAM and HQSAM, to enhance segmentation performance. Our extensive experiments on multiple datasets, encompassing various anatomies and modalities, reveal that FMs, particularly with the HQHSAM decode head, improve domain generalization for medical image segmentation. Moreover, we found that the effectiveness of PEFT techniques varies across different FMs. These findings underscore the potential of FMs to enhance the domain generalization performance of neural networks in medical image segmentation across diverse clinical settings, providing a solid foundation for future research. Code and models are available for research purposes at \url{https://github.com/kerem-cekmeceli/Foundation-Models-for-Medical-Imagery}.