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

Zeghlache, R., Conze, P., Daho, M., Li, Y., Rezaei, A., Le Boité, H., Tadayoni, R., Massin, P., Cochener, B., Brahim, I., & others

Computers in Biology and Medicine, 185, 109508

Automated Multimodal Severity Assessment of Diabetic Retinopathy Using Ultra-Widefield Color Fundus Photography and Clinical Tabular Data

Rezaei, A., Zeghlache, R., Conze, P., Lepicard, C., Deman, P., Borderie, L., Cosette, D., Bonnin, S., Couturier, A., Cochener, B., & others

Available at SSRN 5143621

Automated classification of tertiary lymphoid structures in colorectal cancer using TLS-PAT artificial intelligence tool

Le Rochais, M., Brahim, I., Zeghlache, R., Redoulez, G., Guillard, M., Le Noac’h, P., Castillon, M., Bourhis, A., & Uguen, A.

Scientific Reports, 15(1), 1-12

Sjögren's Syndrome Diagnosis Using Dry Eye Clinical Data using Deep Learning

Lamard, M., Benyoussef, A., Zeghlache, R., Cornec, D., & Brahim, I.

Investigative Ophthalmology & Visual Science, 65(7), 5727-5727

Longitudinal and multi-modal monitoring for the detection of diabetic retinopathy using deep learning

Zeghlache, R.

LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression

Zeghlache, R., Conze, P., El Habib Daho, M., Li, Y., Le Boité, H., Tadayoni, R., Massin, P., Cochener, B., Rezaei, A., Brahim, I., & others

International Conference on Medical Image Computing and Computer-Assisted Intervention, 404-414

DISCOVER: 2-D multiview summarization of Optical Coherence Tomography Angiography for automatic diabetic retinopathy diagnosis

Daho, M., Li, Y., Zeghlache, R., Le Boité, H., Deman, P., Borderie, L., Ren, H., Mannivanan, N., Lepicard, C., Cochener, B., & others

Artificial Intelligence in Medicine, 149, 102803

Cross-Device AI Fusion: Enhancing Diabetic Retinopathy Diagnosis with Combined Clarus and Optos Images

DAHO, M., Li, Y., Zeghlache, R., Rezaei, A., Le Boité, H., Couturier, A., Bonnin, S., Magazzeni, S., Le Guilcher, A., Potevin, F., & others

Investigative Ophthalmology & Visual Science, 65(7), 5630-5630

AB0800 DIAGNOSING SJOGREN’S SYNDROME: A MULTI-MODAL DEEP LEARNING APPROACH WITH HISTOPATHOLOGIC IMAGES AND CLINICAL DATA

Brahim, I., Uguen, A., Zeghlache, R., Devauchelle-Pensec, V., & Cornec, D.

Annals of the Rheumatic Diseases, 83, 1694

A review of deep learning-based information fusion techniques for multimodal medical image classification

Li, Y., Daho, M., Conze, P., Zeghlache, R., Le Boité, H., Tadayoni, R., Cochener, B., Lamard, M., & Quellec, G.

Computers in Biology and Medicine, 108635

Time-aware deep models for predicting diabetic retinopathy progression

zeghlache, R., Conze, P., DAHO, M., LI, Y., Brahim, I., Le Boité, H., Massin, P., Tadayoni, R., Cochener, B., Quellec, G., & others

Investigative Ophthalmology & Visual Science, 64(8), 246-246

Performance of two ultra-widefield retinal imaging systems for the automatic diagnosis of diabetic retinopathy

DAHO, M., Zeghlache, R., LI, Y., Le Boité, H., Bonnin, S., Magazzeni, S., Borderie, L., Lay, B., Tadayoni, R., Cochener, B., & others

Investigative Ophthalmology & Visual Science, 64(8), 251-251

Longitudinal self-supervised learning using neural ordinary differential equation

Zeghlache, R., Conze, P., Daho, M., Li, Y., Boité, H., Tadayoni, R., Massin, P., Cochener, B., Brahim, I., Quellec, G., & others

International Workshop on PRedictive Intelligence In MEdicine, 1-13

Lmt: Longitudinal mixing training, a framework to predict disease progression from a single image

Zeghlache, R., Conze, P., Daho, M., Li, Y., Le Boité, H., Tadayoni, R., Massin, P., Cochener, B., Brahim, I., Quellec, G., & others

International Workshop on Machine Learning in Medical Imaging, 22-32

Improved automatic diabetic retinopathy severity classification using deep multimodal fusion of UWF-CFP and OCTA images

El Habib Daho, M., Li, Y., Zeghlache, R., Atse, Y., Le Boité, H., Bonnin, S., Cosette, D., Deman, P., Borderie, L., Lepicard, C., & others

International Workshop on Ophthalmic Medical Image Analysis, 11-20

Hybrid fusion of high-resolution and ultra-widefield OCTA acquisitions for the automatic diagnosis of diabetic retinopathy

Li, Y., El Habib Daho, M., Conze, P., Zeghlache, R., Le Boité, H., Bonnin, S., Cosette, D., Magazzeni, S., Lay, B., Le Guilcher, A., & others

Diagnostics, 13(17), 2770

3-D analysis of multiple OCTA acquisitions for the automatic diagnosis of diabetic retinopathy

LI, Y., DAHO, M., Conze, P., Zeghlache, R., Ren, H., Lepicard, C., Deman, P., Le Guilcher, A., Cochener, B., Tadayoni, R., & others

Investigative Ophthalmology & Visual Science, 64(8), 279-279

Segmentation, classification, and quality assessment of UW-octa images for the diagnosis of diabetic retinopathy

Li, Y., Zeghlache, R., Brahim, I., Xu, H., Tan, Y., Conze, P., Lamard, M., Quellec, G., & El Habib Daho, M.

MICCAI Challenge on Mitosis Domain Generalization, 146-160

Driver vigilance estimation with Bayesian LSTM Auto-encoder and XGBoost using EEG/EOG data

Zeghlache, R., Labiod, M., & Mellouk, A.

IFAC-PapersOnLine, 55(8), 89-94

Detection of diabetic retinopathy using longitudinal self-supervised learning

Zeghlache, R., Conze, P., Daho, M., Tadayoni, R., Massin, P., Cochener, B., Quellec, G., & Lamard, M.

International Workshop on Ophthalmic Medical Image Analysis, 43-52