Problem
Research questions and friction points this paper is trying to address.
Medical Foundation Model
Domain Adaptation
Cross-Modality
Cross-Center
Parameter-Efficient Fine-Tuning
FAN-LoRA: A Fourier-Adaptive Nonlinear Low-Rank Adaptor for Medical Foundation Model Domain Adaptation
🤖 AI Summary
本文提出FAN-LoRA方法，通过频率解耦优化空间，解决医学图像领域适应中的性能下降问题。
📝 Abstract
The advent of vision foundation models, notably the Segment Anything Model (SAM), has catalyzed significant advancements in natural image segmentation. However, their direct transfer to medical imaging remains severely bottlenecked by profound domain gaps, such as cross-modality and cross-center shifts. Existing Parameter-Efficient Fine-Tuning (PEFT) methods facilitate the adaptation of SAM to medical domains; nevertheless, they frequently suffer from performance degradation under severe distribution shifts. This vulnerability primarily stems from the implicit entanglement of heterogeneous frequency components within a shared low-rank subspace, which directly exacerbates sub-optimal structural alignment and localized boundary blurring. To overcome this representational bottleneck, we propose the Fourier-Adaptive Nonlinear Low-Rank Adaptor (FAN-LoRA), a novel frequency-decoupled fine-tuning architecture. FAN-LoRA explicitly separates the optimization space by employing a B-spline-driven low-pass branch for global structural alignment, synergistically coupled with a discrete Fourier high-pass branch for local textural compensation. Extensive experiments across three challenging cross-modality and cross-center benchmarks demonstrate that FAN-LoRA consistently outperforms state-of-the-art PEFT baselines. Compared to the strongest competitors, our method achieves consistent improvements in average Dice scores and notable reductions in boundary errors, while maintaining a compact module size without compromising computational efficiency.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Fourier-Adaptive Nonlinear Low-Rank Adaptor
Frequency-Decoupled Fine-Tuning
B-spline-driven Low-Pass Branch
Discrete Fourier High-Pass Branch
Medical Foundation Model Domain Adaptation
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