Open AccessFederated Learning for Medical Imaging: An Updated State of the Art
Author(s) -
Naoual Mouhni,
Abderrafiaa Elkalay,
Mohamed Chakraoui,
Abdelmounaïm Abdali,
Abdelkarim Ammoumou,
Ibtissam Amalou
Publication year - 2022
Publication title -
ingénierie des systèmes d'information/ingénierie des systèmes d'information
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.161
H-Index - 8
eISSN - 2116-7125
pISSN - 1633-1311
DOI - 10.18280/isi.270117
Subject(s) - computer science , convolutional neural network , artificial intelligence , deep learning , medical imaging , machine learning , training set , federated learning , state (computer science) , artificial neural network , algorithm
Deep Neural networks algorithms are recently used to solve problems in medical imaging like no time ever. However, one of the main challenges for training robust and accurate machine learning algorithms, such as Convolutional neural networks (CNNs) is to find a large dataset, which is, unfortunately, not available for public usage, or it is not available when it comes to a rare disease. Federated Learning (FL) could be a solution to data lack. It can make training and validation through multicenter datasets possible, without compromising the privacy and data protection. In this paper we summarize, discuss, and present an UpToDate overview of FL for medical image analysis solutions and related approaches.