21 citations · 61 across the 15 of their papers we have counts for
16 papers
CornOrb: A Multimodal Dataset of Orbscan Corneal Topography and Clinical Annotations for Keratoconus Detection
Mohammed El Amine Lazouni, Leila Ryma Lazouni, Zineb Aziza Elaouaber +8
In this paper, we present CornOrb, a publicly accessible multimodal dataset of Orbscan corneal topography images and clinical annotations collected from patients in Algeria. The da…
Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge
Rachid Zeghlache, Ikram Brahim, Pierre-Henri Conze +47
The MARIO challenge, held at MICCAI 2024, focused on advancing the automated detection and monitoring of age-related macular degeneration (AMD) through the analysis of optical cohe…
Deep Learning-Based Detection of Referable Diabetic Retinopathy and Macular Edema Using Ultra-Widefield Fundus Imaging
Philippe Zhang, Pierre-Henri Conze, Mathieu Lamard +2
Diabetic retinopathy and diabetic macular edema are significant complications of diabetes that can lead to vision loss. Early detection through ultra-widefield fundus imaging enhan…
A review of deep learning-based information fusion techniques for multimodal medical image classification
Yihao Li, Mostafa El Habib Daho, Pierre-Henri Conze +6
Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various imaging modalities to provide a more comprehensive under…
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho +9
This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE). We introduce a "time-aware head" in a framewo…
L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction
Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho +9
Pre-training strategies based on self-supervised learning (SSL) have proven to be effective pretext tasks for many downstream tasks in computer vision. Due to the significant dispa…
DISCOVER: 2-D Multiview Summarization of Optical Coherence Tomography Angiography for Automatic Diabetic Retinopathy Diagnosis
Mostafa El Habib Daho, Yihao Li, Rachid Zeghlache +12
Diabetic Retinopathy (DR), an ocular complication of diabetes, is a leading cause of blindness worldwide. Traditionally, DR is monitored using Color Fundus Photography (CFP), a wid…
Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction
Yihao Li, Philippe Zhang, Yubo Tan +7
Myopic macular degeneration is the most common complication of myopia and the primary cause of vision loss in individuals with pathological myopia. Early detection and prompt treat…
Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation
Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho +8
Longitudinal analysis in medical imaging is crucial to investigate the progressive changes in anatomical structures or disease progression over time. In recent years, a novel class…
LMT: Longitudinal Mixing Training, a Framework to Predict Disease Progression from a Single Image
Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho +8
Longitudinal imaging is able to capture both static anatomical structures and dynamic changes in disease progression toward earlier and better patient-specific pathology management…
Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Images
Mostafa El Habib Daho, Yihao Li, Rachid Zeghlache +12
Diabetic Retinopathy (DR), a prevalent and severe complication of diabetes, affects millions of individuals globally, underscoring the need for accurate and timely diagnosis. Recen…
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images
Bo Qian, Hao Chen, Xiangning Wang +18
Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomog…
Multimodal Information Fusion For The Diagnosis Of Diabetic Retinopathy
Yihao Li, Hassan Al Hajj, Pierre-Henri Conze +8
Diabetes is a chronic disease characterized by excess sugar in the blood and affects 422 million people worldwide, including 3.3 million in France. One of the frequent complication…
Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy
Yihao Li, Rachid Zeghlache, Ikram Brahim +6
Diabetic Retinopathy (DR) is a severe complication of diabetes that can cause blindness. Although effective treatments exist (notably laser) to slow the progression of the disease…
Multimodal Information Fusion for Glaucoma and DR Classification
Yihao Li, Mostafa El Habib Daho, Pierre-Henri Conze +9
Multimodal information is frequently available in medical tasks. By combining information from multiple sources, clinicians are able to make more accurate judgments. In recent year…
Detection of diabetic retinopathy using longitudinal self-supervised learning
Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho +5
Longitudinal imaging is able to capture both static anatomical structures and dynamic changes in disease progression towards earlier and better patient-specific pathology managemen…