Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift
This addresses reliability issues for deploying medical VLMs in real-world clinical settings, representing an incremental improvement with specific gains.
The paper tackled the problem of medical vision-language models failing under distribution shifts by proposing DRiFt, a feature decoupling framework, which improved in-distribution performance by +11.4% Top-1 accuracy and +3.3% Macro-F1 over prior methods while maintaining robustness across unseen datasets.
Medical vision-language models (VLMs) offer promise for clinical decision support, yet their reliability under distribution shifts remains a major concern for safe deployment. These models often learn task-agnostic correlations due to variability in imaging protocols and free-text reports, limiting their generalizability and increasing the risk of failure in real-world settings. We propose DRiFt, a structured feature decoupling framework that explicitly separates clinically relevant signals from task-agnostic noise using parameter-efficient tuning (LoRA) and learnable prompt tokens. To enhance cross-modal alignment and reduce uncertainty, we curate high-quality, clinically grounded image-text pairs by generating captions for a diverse medical dataset. Our approach improves in-distribution performance by +11.4% Top-1 accuracy and +3.3% Macro-F1 over prior prompt-based methods, while maintaining strong robustness across unseen datasets. Ablation studies reveal that disentangling task-relevant features and careful alignment significantly enhance model generalization and reduce unpredictable behavior under domain shift. These insights contribute toward building safer, more trustworthy VLMs for clinical use. The code is available at https://github.com/rumaima/DRiFt.