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September 2020 - February 2021
February 2020 - March 2020
- PhD Student
- Ph.D in Computer Science and Software Engineer. I'm focusing my research efforts on distributed deep-learning-based medical imaging applications.
Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep learning algorithms, outperforms the results of classical segmentation in terms of accuracy. However,...
Une plateforme d'apprentissage profond à base de composants qui passe à l'échelle : une application aux réseaux de neurones convolutionnels pour la segmentation en imagerie médicale par Soulaimane Guedria Thèse de doctorat en Informatique Sous la direction de Noël de Palma et de Nicolas Vuillerme. Soutenue le 08-07-2020 à l'Université Grenoble A...
Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine...
Effectively training of Convolutional Neural Networks (CNNs) is a computationally intensive and time-consuming task. Therefore, scaling up the training of CNNs has become a key approach to decrease the training duration and train CNN models in a reasonable time. Nevertheless, introducing parallelism to CNNs is a laborious task in practice. It is a...
Training Convolutional Neural Networks (CNNs) is a computationally intensive and time-consuming task. For this reason, distributing the training process of CNNs has become a crucial approach to decrease the training duration and effectively train CNN models in a reasonable time. Nevertheless, introducing parallelism to CNNs is a challenging task in...