MFM-DA: Instance-Aware Adaptor and Hierarchical Alignment for Efficient Domain Adaptation in Medical Foundation Models
Journal:
arXiv
Published Date:
Mar 2, 2025
Abstract
Medical Foundation Models (MFMs), trained on large-scale datasets, have
demonstrated superior performance across various tasks. However, these models
still struggle with domain gaps in practical applications. Specifically, even
after fine-tuning on source-domain data, task-adapted foundation models often
perform poorly in the target domain. To address this challenge, we propose a
few-shot unsupervised domain adaptation (UDA) framework for MFMs, named MFM-DA,
which only leverages a limited number of unlabeled target-domain images. Our
approach begins by training a Denoising Diffusion Probabilistic Model (DDPM),
which is then adapted to the target domain using a proposed dynamic
instance-aware adaptor and a distribution direction loss, enabling the DDPM to
translate source-domain images into the target domain style. The adapted images
are subsequently processed through the MFM, where we introduce a designed
channel-spatial alignment Low-Rank Adaptation (LoRA) to ensure effective
feature alignment. Extensive experiments on optic cup and disc segmentation
tasks demonstrate that MFM-DA outperforms state-of-the-art methods. Our work
provides a practical solution to the domain gap issue in real-world MFM
deployment. Code will be available at here.
