79 citations · 110 across the 11 of their papers we have counts for
13 papers
3D Transformer based on deformable patch location for differential diagnosis between Alzheimer's disease and Frontotemporal dementia
Huy-Dung Nguyen, Michaël Clément, Boris Mansencal +1
Alzheimer's disease and Frontotemporal dementia are common types of neurodegenerative disorders that present overlapping clinical symptoms, making their differential diagnosis very…
Brain Structure Ages -- A new biomarker for multi-disease classification
Huy-Dung Nguyen, Michaël Clément, Boris Mansencal +1
Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some…
Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022
Pierre-Etienne Martin, Jordan Calandre, Boris Mansencal +4
Sports video analysis is a widespread research topic. Its applications are very diverse, like events detection during a match, video summary, or fine-grained movement analysis of a…
Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis
Huy-Dung Nguyen, Michaël Clément, Boris Mansencal +1
Accurate diagnosis and prognosis of Alzheimer's disease are crucial to develop new therapies and reduce the associated costs. Recently, with the advances of convolutional neural ne…
Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia
Huy-Dung Nguyen, Michaël Clément, Vincent Planche +2
Alzheimer's disease and Frontotemporal dementia are common forms of neurodegenerative dementia. Behavioral alterations and cognitive impairments are found in the clinical courses o…
Longitudinal detection of new MS lesions using Deep Learning
Reda Abdellah Kamraoui, Boris Mansencal, José V Manjon +1
The detection of new multiple sclerosis (MS) lesions is an important marker of the evolution of the disease. The applicability of learning-based methods could automate this task ef…
Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia
Huy-Dung Nguyen, Michaël Clément, Boris Mansencal +1
Alzheimer's disease and Frontotemporal dementia are two major types of dementia. Their accurate diagnosis and differentiation is crucial for determining specific intervention and t…
Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence
Huy-Dung Nguyen, Michaël Clément, Boris Mansencal +1
Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis and prognosis of this disease are essential to design an appropriate treatment plan, increasing the lif…
Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021
Pierre-Etienne Martin, Jordan Calandre, Boris Mansencal +4
Sports video analysis is a prevalent research topic due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests up to analysis o…
DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation
Reda Abdellah Kamraoui, Vinh-Thong Ta, Thomas Tourdias +3
Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These technique…
Move-to-Data: A new Continual Learning approach with Deep CNNs, Application for image-class recognition
Miltiadis Poursanidis, Jenny Benois-Pineau, Akka Zemmari +2
In many real-life tasks of application of supervised learning approaches, all the training data are not available at the same time. The examples are lifelong image classification o…
AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation
Pierrick Coupé, Boris Mansencal, Michaël Clément +5
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.…
AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
Pierrick Coupé, Boris Mansencal, Michaël Clément +5
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.…