FAN-LoRA：用于医学基础模型领域适应的傅里叶自适应非线性低秩适配器
FAN-LoRA: A Fourier-Adaptive Nonlinear Low-Rank Adaptor for Medical Foundation Model Domain Adaptation
- School of Information and Control Engineering, Southwest University of Science and Technology（西南科技大学信息与控制工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对SAM迁移至医学成像的领域差距问题，提出FAN-LoRA架构，通过频率解耦微调实现结构与纹理补偿，在三类基准上优于现有PEFT方法，提升分割性能且兼顾效率。
AI中文摘要：
视觉基础模型，尤其是Segment Anything Model（SAM）的出现，推动了自然图像分割领域的重大进展。然而，将其直接迁移到医学成像领域仍受严重领域差距的制约，例如跨模态和跨中心偏移。现有的参数高效微调（PEFT）方法可促进SAM适应医学领域，但在严重分布偏移下常出现性能下降。该缺陷主要源于共享低秩子空间内异构频率分量的隐式纠缠，这直接加剧了次优结构对齐和局部边界模糊。为克服这一表征瓶颈，我们提出傅里叶自适应非线性低秩适配器（FAN-LoRA），一种新型频率解耦微调架构。FAN-LoRA通过采用B样条驱动的低通分支进行全局结构对齐，协同耦合离散傅里叶高通分支进行局部纹理补偿，明确分离优化空间。在三个具有挑战性的跨模态和跨中心基准上开展的大量实验表明，FAN-LoRA始终优于最先进的PEFT基线。与最强竞争者相比，我们的方法在平均Dice分数上实现了一致提升，边界误差显著降低，同时保持紧凑的模块规模，且不损害计算效率。
英文摘要：
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.
