526 citations · 783 across the 36 of their papers we have counts for
38 papers
Constrained latent state modeling: A unifying perspective on representation learning under competing constraints
Gwenolé Quellec
Learning latent representations from temporal, multimodal, and partially observed data requires specifying what information a latent state should retain, discard, and organize. Exi…
Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization
Ambre Marie, Thomas Bertin, Guillaume Dardenne +1
Automatic speech recognition for French medical conversations remains challenging, with word error rates often exceeding 30% in spontaneous clinical speech. This study proposes a m…
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…
Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset
Ambre Marie, Ilias Maoudj, Guillaume Dardenne +1
The 1st SpeechWellness Challenge conveys the need for speech-based suicide risk assessment in adolescents. This study investigates a multimodal approach for this challenge, integra…
Acoustic and Machine Learning Methods for Speech-Based Suicide Risk Assessment: A Systematic Review
Ambre Marie, Marine Garnier, Thomas Bertin +4
Suicide remains a public health challenge, necessitating improved detection methods to facilitate timely intervention and treatment. This systematic review evaluates the role of Ar…
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…
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…
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…
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…
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…
Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images
Hui Xu, Yihao Li, Wei Zhao +3
Automatic segmentation of head and neck cancer (HNC) tumors and lymph nodes plays a crucial role in the optimization treatment strategy and prognosis analysis. This study aims to e…
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…