Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Quellec, Gwenole"'
Autor:
Zhang, Philippe, Conze, Pierre-Henri, Lamard, Mathieu, Quellec, Gwenolé, Daho, Mostafa El Habib
Diabetic retinopathy and diabetic macular edema are significant complications of diabetes that can lead to vision loss. Early detection through ultra-widefield fundus imaging enhances patient outcomes but presents challenges in image quality and anal
Externí odkaz:
http://arxiv.org/abs/2409.12854
Autor:
Li, Yihao, Daho, Mostafa El Habib, Conze, Pierre-Henri, Zeghlache, Rachid, Boité, Hugo Le, Tadayoni, Ramin, Cochener, Béatrice, Lamard, Mathieu, Quellec, Gwenolé
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 understanding of the underlying pathology. Recently, deep learning-based mu
Externí odkaz:
http://arxiv.org/abs/2404.15022
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
Autor:
Zeghlache, Rachid, Conze, Pierre-Henri, Daho, Mostafa El Habib, Li, Yihao, Boité, Hugo Le, Tadayoni, Ramin, Massin, Pascal, Cochener, Béatrice, Rezaei, Alireza, Brahim, Ikram, Quellec, Gwenolé, Lamard, Mathieu
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 framework trained through self-supervised learning (SSL) to leverage temporal
Externí odkaz:
http://arxiv.org/abs/2404.07091
Autor:
Zeghlache, Rachid, Conze, Pierre-Henri, Daho, Mostafa El Habib, Li, Yihao, Rezaei, Alireza, Boité, Hugo Le, Tadayoni, Ramin, Massin, Pascal, Cochener, Béatrice, Brahim, Ikram, Quellec, Gwenolé, Lamard, Mathieu
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 disparity between medical and natural images, the application of typical SS
Externí odkaz:
http://arxiv.org/abs/2403.16272
Autor:
Matta, Sarah, Lamard, Mathieu, Zhang, Philippe, Guilcher, Alexandre Le, Borderie, Laurent, Cochener, Béatrice, Quellec, Gwenolé
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 medical practice. Despite early advances in DL model validation and impleme
Externí odkaz:
http://arxiv.org/abs/2403.12167
Autor:
Daho, Mostafa El Habib, Li, Yihao, Zeghlache, Rachid, Boité, Hugo Le, Deman, Pierre, Borderie, Laurent, Ren, Hugang, Mannivanan, Niranchana, Lepicard, Capucine, Cochener, Béatrice, Couturier, Aude, Tadayoni, Ramin, Conze, Pierre-Henri, Lamard, Mathieu, Quellec, Gwenolé
Publikováno v:
Artificial Intelligence in Medicine 2024, 102803
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 widespread 2-D imaging modality. However, DR classifications based on CFP
Externí odkaz:
http://arxiv.org/abs/2401.05137
Autor:
Li, Yihao, Zhang, Philippe, Tan, Yubo, Zhang, Jing, Wang, Zhihan, Jiang, Weili, Conze, Pierre-Henri, Lamard, Mathieu, Quellec, Gwenolé, Daho, Mostafa El Habib
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 treatment are crucial in preventing vision impairment due to myopic maculop
Externí odkaz:
http://arxiv.org/abs/2401.03615
Autor:
Zeghlache, Rachid, Conze, Pierre-Henri, Daho, Mostafa El Habib, Li, Yihao, Boité, Hugo Le, Tadayoni, Ramin, Massin, Pascal, Cochener, Béatrice, Brahim, Ikram, Quellec, Gwenolé, Lamard, Mathieu
Publikováno v:
Predictive Intelligence in Medicine. PRIME 2023. Part of the Lecture Notes in Computer Science book series (LNCS,volume 14277)
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 of algorithms has emerged with the goal of learning disease progressi
Externí odkaz:
http://arxiv.org/abs/2310.10431
Autor:
Zeghlache, Rachid, Conze, Pierre-Henri, Daho, Mostafa El Habib, Li, Yihao, boite, Hugo Le, Tadayoni, Ramin, Massin, Pascal, Cochener, Béatrice, Brahim, Ikram, Quellec, Gwenolé, Lamard, Mathieu
Publikováno v:
Machine Learning in Medical Imaging. MLMI 2023
Longitudinal imaging is able to capture both static anatomical structures and dynamic changes in disease progression toward earlier and better patient-specific pathology management. However, conventional approaches rarely take advantage of longitudin
Externí odkaz:
http://arxiv.org/abs/2310.10420
Autor:
Daho, Mostafa El Habib, Li, Yihao, Zeghlache, Rachid, Atse, Yapo Cedric, Boité, Hugo Le, Bonnin, Sophie, Cosette, Deborah, Deman, Pierre, Borderie, Laurent, Lepicard, Capucine, Tadayoni, Ramin, Cochener, Béatrice, Conze, Pierre-Henri, Lamard, Mathieu, Quellec, Gwenolé
Diabetic Retinopathy (DR), a prevalent and severe complication of diabetes, affects millions of individuals globally, underscoring the need for accurate and timely diagnosis. Recent advancements in imaging technologies, such as Ultra-WideField Color
Externí odkaz:
http://arxiv.org/abs/2310.01912