Publication – EviRed

Automated multimodal severity assessment of diabetic retinopathy using ultra-widefield color fundus photography and clinical tabular data

Rezaei, A. et al.

Biomedical Signal Processing and Control,Volume 112, Part C,2026,108673

https://doi.org/10.1016/j.bspc.2025.108673

Deep learning for retinal non-perfusion and foveal avascular zone analysis in wide-field OCTA in diabetic retinopathy

Le Boité, H. et al.

Sci Rep 15, 30225 (2025).

https://doi.org/10.1038/s41598-025-15712-3

MARIO: Monitoring Age-related Macular Degeneration Progression In Optical Coherence Tomography

This dataset was created for the MARIO challenge, held as a satellite event of the MICCAI 2024 conference.

Data managers : Gwenole Quellec , Rachid Zeghlache

https://zenodo.org/records/15270469

Image-Based Prediction of Retinal Disease Progression

MICCAI Challenges, DIAMOND 2024 and MARIO 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings

Editors: Gwenole Quellec, Mostafa El Habib Daho, Rachid Zeghlache

https://link.springer.com/book/10.1007/978-3-031-86651-7

L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction

R. Zeghlache et al.

Computers in Biology and Medicine 2025; 185:109508

https://doi.org/10.1016/j.compbiomed.2024.109508

“Spatially resolved imaging of human macular capillaries using adaptive optics-enhanced optical coherence tomography angiography”

Bonnin S and al.

Sci Rep. 2024 Jul 5;14(1):15540. PMID: 38969668; PMCID: PMC11226425

https://doi.org/10.1038/s41598-024-65534-y

This work benefits from State aid managed by the National Research Agency under the investment program for the Future integrated to France 2030 bearing the reference ANR-18-RHUS-0008

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