Soulaimane Guedria

Soulaimane Guedria
Université Grenoble Alpes · Computer Science

Ph.D.

About

5
Publications
978
Reads
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85
Citations
Additional affiliations
September 2020 - February 2021
Laboratoire d'Informatique de Grenoble
Position
  • Researcher
Description
  • Development and implementation of a deep-learning-based solution for rapid detection and diagnostic of Covid-19
February 2020 - March 2020
Laboratoire d'Informatique de Grenoble
Position
  • PhD Student
Description
  • Ph.D in Computer Science and Software Engineer. I'm focusing my research efforts on distributed deep-learning-based medical imaging applications.
Education
February 2017 - June 2020
Université Grenoble Alpes
Field of study
  • Computer Science
September 2011 - September 2016

Publications

Publications (5)
Article
Full-text available
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,...
Thesis
Full-text available
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...
Article
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...
Conference Paper
Full-text available
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...
Conference Paper
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...

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