TAPE：一种用于OCT-OCTA分析的foundation models的两阶段参数高效适应框架
TAPE: A two-stage parameter-efficient adaptation framework for foundation models in OCT-OCTA analysis
AI总结：
本文提出TAPE框架，通过两阶段参数高效微调解决OCT-OCTA分析中的领域偏移和任务不匹配问题，实现更高效的模型适应与更广的病理泛化性能。
摘要：
自动分析光学相干断层扫描（OCT）和OCT angiography（OCTA）图像对于稳健的眼科诊断至关重要。现有主流方法从头训练依赖于大量数据和模型规模，从而阻碍了其在资源受限的临床环境中的实际部署。尽管基于foundation models（FMs）的迁移学习具有前景，但仍面临显著挑战：领域偏移和任务不匹配。为了解决这些问题，我们提出TAPE：一种通过参数高效微调的两阶段适应框架，战略性地将适应分解为领域对齐和任务适配，以实现下游分割。领域适应阶段显著应用参数高效微调（PEFT）在遮蔽图像建模的背景下进行医学图像领域适应，这是一种新的方法。将TAPE应用于通用（遮蔽自动编码器，MAE）和专用（RETFound）FMs上的视网膜层分割，它在参数效率和跨多种病理学的泛化性能方面均表现出色。
英文摘要：
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.
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