TAPE: A two-stage parameter-efficient adaptation framework for foundation models in OCT-OCTA analysis

TL;DR

TAPE is a novel two-stage parameter-efficient framework that enhances foundation model adaptation for OCT-OCTA image analysis, improving generalization and efficiency in ophthalmic diagnosis.

Contribution

It introduces a two-stage adaptation process with a novel PEFT approach for medical image domain adaptation, achieving state-of-the-art results.

Findings

Superior generalization across diverse pathologies.

Achieves state-of-the-art segmentation performance.

Demonstrates high parameter efficiency.

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.