AI RESEARCH
TAPE: A two-stage parameter-efficient adaptation framework for foundation models in OCT-OCTA analysis
arXiv CS.CV
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ArXi:2604.04571v1 Announce Type: new 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.