Abstract
Automated analysis of optical coherence tomography (OCT) and OCT angiography (OCTA) images is critical for robust ophthalmic diagnosis. Existing mainstream methods trained from scratch rely heavily on massive data and model scale, thereby hindering their practical deployment in resource-constrained clinical settings. Although transfer learning based on foundation models (FMs) is promising, it still faces significant challenges: domain shift and task misalignment. To address these, we propose TAPE: A Two-stage Adaptation Framework via Parameter-Efficient Fine-tuning, which strategically decouples adaptation into domain alignment and task fitting for downstream segmentation. The domain adaptation stage notably applies parameter-efficient fine-tuning (PEFT) in the context of masked image modeling for medical image domain adaptation, a novel approach to the best of our knowledge. Applying TAPE to retinal layer segmentation on both universal (masked auto-encoder, MAE) and specialized (RETFound) FMs, it demonstrates superior parameter efficiency and achieves state-of-the-art generalization performance across diverse pathologies.
Abstract (translated)
光学相干断层扫描（OCT）与OCT血管成像（OCTA）图像的自动化分析对于稳健的眼科诊断至关重要。现有从零训练的主流方法严重依赖大规模数据与模型参数，从而阻碍了其在资源受限临床环境中的实际部署。尽管基于基础模型（FMs）的迁移学习前景可观，但仍面临领域偏移与任务错位两大挑战。针对这些问题，我们提出TAPE：一种通过参数高效微调实现的两阶段适应框架，该框架将下游分割任务的适应过程策略性地解耦为领域对齐与任务拟合两个阶段。领域适应阶段创新性地在掩码图像建模的医学图像领域适应场景中应用参数高效微调（PEFT），据我们所知此为首次提出。将TAPE应用于视网膜层分割任务时，无论在通用基础模型（掩码自编码器MAE）还是专业基础模型（RETFound）上，均展现出卓越的参数效率，并在多种病理条件下达到最先进的泛化性能。
URL
https://arxiv.org/abs/2604.04571
