Topology-Aware Parameter-Efficient Adaptation for Cross-Dataset Retinal Vessel Segmentation
Retinal vessel segmentation in multi-domain deployment requires a source model to adapt to domains that differ in imaging conditions and annotation conventions. Conventional parameter-efficient fine-tuning reduces target-specific storage, but its highly restricted adaptation subspace can be insufficient for reconstructing thin, connected vascular structures. We therefore ask how target-specific capacity should be allocated so that topology-aware supervision remains effective under a strict per-domain parameter budget. Based on this principle, we propose TAPDecoderFT, a topology-responsive, role-structured adaptation framework. Specifically, TAPDecoderFT shares a fixed source parameter sta...
| Authors | Yongsong Huang, Tomo Miyazaki, Kai Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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