Adapters have become a widely adopted strategy for efficient fine-tuning of large pretrained models, particularly in resource-constrained settings. However, their performance under extreme data scarcity, common in medical imaging due to high annotation costs, privacy regulations, and fragmented datasets, remains underexplored. In this work, we present the first comprehensive study of adapter-based fine-tuning for large pretrained models in low-data medical imaging scenarios. We find that, contrary to their promise, conventional adapters can degrade performance under severe data constraints, performing even worse than simple linear probing when trained on less than 1% of the corresponding training data. Through systematic analysis, we identify a sharp reduction in Effective Receptive Field (ERF) as a key factor behind this degradation. Motivated by these findings, we propose the Dual-Kernel Adapter (DKA), a lightweight module that expands spatial context via large-kernel convolutions while preserving local detail with small-kernel counterparts. Extensive experiments across diverse classification and segmentation benchmarks show that DKA significantly outperforms existing adapter methods, establishing new leading results in both data-constrained and data-rich regimes.

翻译：适配器已成为高效微调大型预训练模型的广泛采用策略，尤其在资源受限场景中。然而，其在极端数据稀缺条件下的性能——这在医学影像领域因高标注成本、隐私法规和碎片化数据集而普遍存在——仍未得到充分探索。本研究首次对低数据医学影像场景中基于适配器的大型预训练模型微调进行了全面研究。我们发现，与传统认知相反，常规适配器在严重数据约束下可能导致性能下降，当使用少于对应训练数据1%的数据进行训练时，其表现甚至逊于简单的线性探测。通过系统性分析，我们确定有效感受野的急剧缩减是导致此性能退化的关键因素。基于这些发现，我们提出了双核适配器——一种轻量级模块，它通过大核卷积拓展空间上下文信息，同时利用小核卷积保留局部细节。在多种分类与分割基准测试上的广泛实验表明，双核适配器显著优于现有适配器方法，在数据受限和数据充足两种情况下均取得了新的领先结果。