526 citations · 676 across the 23 of their papers we have counts for
23 papers
Patch Progression Masked Autoencoder with Fusion CNN Network for Classifying Evolution Between Two Pairs of 2D OCT Slices
Philippe Zhang, Weili Jiang, Yihao Li +13
Age-related Macular Degeneration (AMD) is a prevalent eye condition affecting visual acuity. Anti-vascular endothelial growth factor (anti-VEGF) treatments have been effective in s…
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-enabled prediction of surgical errors during cataract surgery: from simulation to real-world application
Maxime Faure, Pierre-Henri Conze, Béatrice Cochener +3
Real-time prediction of technical errors from cataract surgical videos can be highly beneficial, particularly for telementoring, which involves remote guidance and mentoring throug…
Context-Aware Vision Language Foundation Models for Ocular Disease Screening in Retinal Images
Lucie Berger, Mathieu Lamard, Philippe Zhang +6
Foundation models are large-scale versatile systems trained on vast quantities of diverse data to learn generalizable representations. Their adaptability with minimal fine-tuning m…
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…
A Systematic Review of Generalization Research in Medical Image Classification
Sarah Matta, Mathieu Lamard, Philippe Zhang +4
Numerous Deep Learning (DL) classification models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medic…
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…
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…
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…
Mapping the ocular surface from monocular videos with an application to dry eye disease grading
Ikram Brahim, Mathieu Lamard, Anas-Alexis Benyoussef +4
With a prevalence of 5 to 50%, Dry Eye Disease (DED) is one of the leading reasons for ophthalmologist consultations. The diagnosis and quantification of DED usually rely on ocular…
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…
ExplAIn: Explanatory Artificial Intelligence for Diabetic Retinopathy Diagnosis
Gwenolé Quellec, Hassan Al Hajj, Mathieu Lamard +3
In recent years, Artificial Intelligence (AI) has proven its relevance for medical decision support. However, the "black-box" nature of successful AI algorithms still holds back th…
Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention
Yutong Yan, Pierre-Henri Conze, Gwenolé Quellec +3
Mammography is the primary imaging modality used for early detection and diagnosis of breast cancer. X-ray mammogram analysis mainly refers to the localization of suspicious region…
Automatic detection of rare pathologies in fundus photographs using few-shot learning
Gwenolé Quellec, Mathieu Lamard, Pierre-Henri Conze +2
In the last decades, large datasets of fundus photographs have been collected in diabetic retinopathy (DR) screening networks. Through deep learning, these datasets were used to tr…
Instant automatic diagnosis of diabetic retinopathy
Gwenolé Quellec, Mathieu Lamard, Bruno Lay +4
The purpose of this study is to evaluate the performance of the OphtAI system for the automatic detection of referable diabetic retinopathy (DR) and the automatic assessment of DR…
Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks
Hassan Al Hajj, Mathieu Lamard, Pierre-Henri Conze +2
This paper investigates the automatic monitoring of tool usage during a surgery, with potential applications in report generation, surgical training and real-time decision support.…
Real-time analysis of cataract surgery videos using statistical models
Katia Charrière, Gwenolé Quellec, Mathieu Lamard +4
The automatic analysis of the surgical process, from videos recorded during surgeries, could be very useful to surgeons, both for training and for acquiring new techniques. The tra…
Deep image mining for diabetic retinopathy screening
Gwenolé Quellec, Katia Charrière, Yassine Boudi +2
Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient path…
Coarse-to-fine Surgical Instrument Detection for Cataract Surgery Monitoring
Hassan Al Hajj, Gwenolé Quellec, Mathieu Lamard +2
The amount of surgical data, recorded during video-monitored surgeries, has extremely increased. This paper aims at improving existing solutions for the automated analysis of catar…