自动优化少样本3D医学图像分割的参数高效微调方法
Self-Auditing Parameter-Efficient Fine-Tuning for Few-Shot 3D Medical Image Segmentation
- 将适配器配置视为在线分配问题，边微调边动态选择最优适配器
- 在1/5/10样本设置下，平均Dice提升2.4–2.8点，参数更新不足1%
- 无需人工设计，适合缺乏AI工程师的临床团队快速部署
将基础模型适配到新临床场景仍具挑战性，需应对领域偏移和标注稀缺问题，但许多临床团队缺乏专业AI工程师来设计适配器结构和训练方案，导致适应周期长达数周至数月，尤其在少样本情形下更为突出。现有参数高效微调（PEFT）方法或依赖人工配置，或采用自动化搜索，但在少样本3D场景中计算开销过大。本文提出自审核参数高效微调（SEA-PEFT），将适配器配置视为微调过程中的在线分配问题，而非固定拓扑结构。SEA-PEFT采用搜索-审计-分配循环：训练活跃适配器，通过临时关闭评估各适配器的骰子系数贡献，再在参数预算下用贪心背包算法重新选择活跃集。结合指数移动平均、四分位距平滑及有限状态排序控制器，增强高噪声少样本环境下的稳定性。在TotalSegmentator与FLARE'22数据集上，SEA-PEFT在1/5/10样本设置下相较最强固定拓扑基线平均提升2.4–2.8个骰子系数，且训练参数占比小于1%。为保障可复现性，代码已公开于https://github.com/tsly123/SEA_PEFT。
原文摘要 · Abstract (English)
Adapting foundation models to new clinical sites remains challenging in practice. Domain shift and scarce annotations must be handled by experts, yet many clinical groups do not have ready access to skilled AI engineers to tune adapter designs and training recipes. As a result, adaptation cycles can stretch from weeks to months, particularly in few-shot settings. Existing PEFT methods either require manual adapter configuration or automated searches that are computationally infeasible in few-shot 3D settings. We propose SEA-PEFT (SElf-Auditing Parameter-Efficient Fine-Tuning) to automate this process. SEA-PEFT treats adapter configuration as an online allocation problem solved during fine-tuning rather than through manual, fixed-topology choices. SEA-PEFT uses a search-audit-allocate loop that trains active adapters, estimates each adapter's Dice utility by momentarily toggling it off, and then reselects the active set under a parameter budget using a greedy knapsack allocator. Exponential Moving Average and Interquartile Range smoothing, together with a Finite-State Ranking controller, stabilize the loop and improve reliability in high-noise few-shot regimes. On TotalSegmentator and FLARE'22, SEA-PEFT improves mean Dice by 2.4--2.8 points over the strongest fixed-topology PEFT baselines across 1/5/10-shot settings while training <1% of parameters. For reproducibility purposes, we made our code publicly available at https://github.com/tsly123/SEA_PEFT
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