831 citations · 1.1k across the 36 of their papers we have counts for
22 papers · 1 filter
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…
Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation
Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu +3
The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in c…
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…
Deep vessel segmentation with joint multi-prior encoding
Amine Sadikine, Bogdan Badic, Enzo Ferrante +4
The precise delineation of blood vessels in medical images is critical for many clinical applications, including pathology detection and surgical planning. However, fully-automated…
Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation
Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu +4
Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this res…
Guidelines for Cerebrovascular Segmentation: Managing Imperfect Annotations in the context of Semi-Supervised Learning
Pierre Rougé, Pierre-Henri Conze, Nicolas Passat +1
Segmentation in medical imaging is an essential and often preliminary task in the image processing chain, driving numerous efforts towards the design of robust segmentation algorit…
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…
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…
Cross-dimensional transfer learning in medical image segmentation with deep learning
Hicham Messaoudi, Ahror Belaid, Douraied Ben Salem +1
Over the last decade, convolutional neural networks have emerged and advanced the state-of-the-art in various image analysis and computer vision applications. The performance of 2D…
Cross-modal tumor segmentation using generative blending augmentation and self training
Guillaume Sallé, Pierre-Henri Conze, Julien Bert +3
\textit{Objectives}: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal…
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…
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors
Arnaud Boutillon, Pierre-Henri Conze, Christelle Pons +2
Clinical diagnosis of the pediatric musculoskeletal system relies on the analysis of medical imaging examinations. In the medical image processing pipeline, semantic segmentation u…
Multi-Structure Deep Segmentation with Shape Priors and Latent Adversarial Regularization
Arnaud Boutillon, Bhushan Borotikar, Christelle Pons +2
Automatic segmentation of the musculoskeletal system in pediatric magnetic resonance (MR) images is a challenging but crucial task for morphological evaluation in clinical practice…
Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network
Arnaud Boutillon, Bhushan Borotikar, Valérie Burdin +1
Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatr…
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…
Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks
Pierre-Henri Conze, Ali Emre Kavur, Emilie Cornec-Le Gall +4
Objective : Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automate…
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
A. Emre Kavur, N. Sinem Gezer, Mustafa Barış +24
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have intr…
Combining Shape Priors with Conditional Adversarial Networks for Improved Scapula Segmentation in MR images
Arnaud Boutillon, Bhushan Borotikar, Valérie Burdin +1
This paper proposes an automatic method for scapula bone segmentation from Magnetic Resonance (MR) images using deep learning. The purpose of this work is to incorporate anatomical…