526 citations · 783 across the 36 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
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…
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…
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…
CaDIS: Cataract Dataset for Image Segmentation
Maria Grammatikopoulou, Evangello Flouty, Abdolrahim Kadkhodamohammadi +5
Video feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons. Scene understanding is crucial to computer assisted intervention…
Unsupervised learning-based long-term superpixel tracking
Pierre-Henri Conze, Florian Tilquin, Mathieu Lamard +2
Finding correspondences between structural entities decomposing images is of high interest for computer vision applications. In particular, we analyze how to accurately track super…
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…