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About this paper
Ziquan Zhu, Hanruo Zhu, Siyuan Lu, Xiang Li, Yanda Meng, Gaojie Jin, Lu Yin, Lijie Hu and 3 more
A new Dual-Kernel Adapter (DKA) is introduced to improve fine-tuning of large pretrained models for medical imaging, particularly in data-scarce environments. DKA addresses the issue of reduced Effective Receptive Field in conventional adapters under low-data conditions, achieving superior performance in classification and segmentation tasks with minimal overhead.
Key insights
- Standard adapters exhibit a sharp reduction in their Effective Receptive Field (ERF) under severe data scarcity, which is a primary reason for their performance degradation in medical imaging.
- Medical images often require a large ERF to capture long-range contextual dependencies for accurate analysis of low contrast, ambiguous boundaries, and small pathological structures.
- The dual-kernel design of DKA effectively addresses this by simultaneously expanding the ERF and retaining local detail, providing a more robust adaptation mechanism for data-constrained medical tasks.
Summary by alphaXiv.
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