Rethinking Diffusion-Based Image Generators for Fundus Fluorescein Angiography Synthesis on Limited Data
This addresses the problem of invasive FFA procedures in ophthalmology by enabling synthetic FFA generation, though it appears incremental as it adapts existing diffusion models to a specific medical domain.
The paper tackles the challenge of generating fundus fluorescein angiography (FFA) images from non-invasive fundus images under limited data constraints, proposing a latent diffusion model framework that achieves state-of-the-art results on limited datasets.
Fundus imaging is a critical tool in ophthalmology, with different imaging modalities offering unique advantages. For instance, fundus fluorescein angiography (FFA) can accurately identify eye diseases. However, traditional invasive FFA involves the injection of sodium fluorescein, which can cause discomfort and risks. Generating corresponding FFA images from non-invasive fundus images holds significant practical value but also presents challenges. First, limited datasets constrain the performance and effectiveness of models. Second, previous studies have primarily focused on generating FFA for single diseases or single modalities, often resulting in poor performance for patients with various ophthalmic conditions. To address these issues, we propose a novel latent diffusion model-based framework, Diffusion, which introduces a fine-tuning protocol to overcome the challenge of limited medical data and unleash the generative capabilities of diffusion models. Furthermore, we designed a new approach to tackle the challenges of generating across different modalities and disease types. On limited datasets, our framework achieves state-of-the-art results compared to existing methods, offering significant potential to enhance ophthalmic diagnostics and patient care. Our code will be released soon to support further research in this field.