Exploring the effectiveness of deep features from domain-specific foundation models in retinal image synthesis

Conference item

The adoption of neural network models in medical imaging has been constrained by strict privacy regulations, limited data availability, high acquisition costs, and demographic biases. Deep generative models offer a promising solution by generating synthetic data that bypasses privacy concerns and addresses fairness by producing samples for under-represented groups. However, unlike natural images, medical imaging requires validation not only for fidelity (e.g., Fréchet Inception Score) but also for morphological and clinical accuracy. This is particularly true for colour fundus retinal imaging, which requires precise replication of the retinal vascular network, including vessel topology, continuity, and thickness. In this study, we investigated whether a distance-based loss function based on deep activation layers of a large foundational model trained on large corpus of domain data, colour fundus imaging, offers advantages over a perceptual loss and edge-detection based loss functions. Our extensive validation pipeline, based on both domain-free and domain specific tasks, suggests that domain-specific deep features do not improve autoencoder image generation. Conversely, our findings highlight the effectiveness of conventional edge detection filters in improving the sharpness of vascular structures in synthetic samples.

Authors: Skórniewska, Z; Papiez, BW

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Host title: Medical Image Understanding and Analysis: 29th Annual Conference, MIUA 2025, Leeds, UK, July 15–17, 2025, Proceedings, Part III
• Pages: 293-305
• Series: Lecture Notes in Computer Science
• Series number: 15918
• Publication date: 2025-07-15
• Acceptance date: 2025-05-12
• Event title: 29th UK Conference on Medical Image Understanding and Analysis (MIUA 2025)
• Event location: Leeds, UK
• Event website: https://conferences.leeds.ac.uk/miua/
• Event start date: 2025-07-15
• Event end date: 2025-07-17
• DOI: 10.1007/978-3-031-98694-9_21
• EISSN: 1611-3349
• ISSN: 0302-9743
• EISBN: 9783031986949
• ISBN: 9783031986932
• Language: English
• Keywords: colour fundus imaging; VQ-GAN; generative modelling