831 citations · 1.1k across the 36 of their papers we have counts for
17 papers · 1 filter
VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography
Ziya Ata Yazıcı, N. Sinem Gezer, İlkay Öksüz +19
Accurate segmentation of hepatic and portal vessels in contrast-enhanced computed tomography angiography (CTA) remains challenging due to complex vascular topology, peripheral visi…
Assessing Pancreatic Ductal Adenocarcinoma Vascular Invasion: the PDACVI Benchmark
M. Riera-Marín, O. K. Sikha, J. Rodríguez-Comas +23
Surgical resection remains the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC), and eligibility depends on accurate assessment of vascular invasion…
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…
Regularized directional representations for medical image registration
Vincent Jaouen, Pierre-Henri Conze, Guillaume Dardenne +2
In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion. Concurrently to these efforts, an i…
Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets
Arnaud Boutillon, Pierre-Henri Conze, Christelle Pons +2
Automatic segmentation of magnetic resonance (MR) images is crucial for morphological evaluation of the pediatric musculoskeletal system in clinical practice. However, the accuracy…
Efficient embedding network for 3D brain tumor segmentation
Hicham Messaoudi, Ahror Belaid, Mohamed Lamine Allaoui +5
3D medical image processing with deep learning greatly suffers from a lack of data. Thus, studies carried out in this field are limited compared to works related to 2D natural imag…
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…
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…
Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders
Pierre-Henri Conze, Sylvain Brochard, Valérie Burdin +2
Automatic segmentation of pathological shoulder muscles in patients with musculo-skeletal diseases is a challenging task due to the huge variability in muscle shape, size, location…
Adaptive strategy for superpixel-based region-growing image segmentation
Mahaman Sani Chaibou, Pierre-Henri Conze, Karim Kalti +2
This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained over-segmentation of the input image, the ima…
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.…