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Books and Theses
- 2020
[b1]Mostefa Ben Naceur:
Deep Neural Networks for the segmentation and classification in Medical Imaging. (Apprentissage en profondeur d'un réseau de neurones pour la segmentation et la classification en Imagerie Médicale). University of Paris-Est, France, 2020
Journal Articles
- 2020
- [j2]Mostefa Ben Naceur
, Mohamed Akil, Rachida Saouli, Rostom Kachouri:
Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Medical Image Anal. 63: 101692 (2020) - 2018
- [j1]Mostefa Ben Naceur, Rachida Saouli, Mohamed Akil, Rostom Kachouri:
Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images. Comput. Methods Programs Biomed. 166: 39-49 (2018)
Conference and Workshop Papers
- 2021
- [c3]Mostefa Ben Naceur, Luc Brun, Olivier Lézoray:
Lightweight Deep Symmetric Positive Definite Manifold Network for Real-Time 3D Hand Gesture Recognition. FG 2021: 1-8 - 2019
- [c2]Mostefa Ben Naceur, Mohamed Akil, Rachida Saouli, Rostom Kachouri:
Deep Convolutional Neural Networks for Brain Tumor Segmentation: Boosting Performance Using Deep Transfer Learning: Preliminary Results. BrainLes@MICCAI (2) 2019: 303-315 - [c1]Mostefa Ben Naceur, Rostom Kachouri, Mohamed Akil, Rachida Saouli:
A New Online Class-Weighting Approach with Deep Neural Networks for Image Segmentation of Highly Unbalanced Glioblastoma Tumors. IWANN (2) 2019: 555-567
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