OCTFlow: Unified conditional generation for balanced classification and annotated segmentation in retinal OCT
Abstract
The scarcity of annotated medical images, compounded by severe class imbalance and labor-intensive pixel-wise annotation, fundamentally constrains the development of robust diagnostic systems in retinal OCT analysis. To address these challenges, we propose OCTFlow, a unified conditional synthesis framework for retinal OCT augmentation under class-label or segmentation-mask guidance. Unlike conventional Latent Diffusion Models (LDMs) that use Variational Autoencoders (VAEs) for perceptual compression, OCTFlow operates in the representation space of a frozen ophthalmic foundation model such as RETFound, incorporating domain-specific retinal representations into latent generation. The framework operates consistently across a range of these encoders. We then employ latent Flow Matching (FM) for efficient conditional synthesis. For task-specific conditioning, we introduce two strategies: (1) Representation-Space Classifier Guidance (RSCG), which uses gradients from a noise-aware latent classifier to encourage target-class generation for underrepresented diseases; and (2) Dynamic Region Masking (DRM), which applies stochastic pathology-mask perturbations to promote diverse paired image-mask synthesis. Extensive experiments on a large-scale curated multi-source OCT classification dataset and two segmentation benchmarks demonstrate that OCTFlow performs best on the reported synthesis metrics among the evaluated generative baselines. More importantly, augmenting the training sets…
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