Standardization of Multicentric Image Datasets with Generative Adversarial Networks
Year: 2019
Type: conference-paper
Abstract: Due to the sensitivity of medical images to acquisition parameters, multicentric image studies often suffer from a lack of homogeneity in terms of statistical characteristics, also known as the center effect. There is therefore a clear need for image-based standardization techniques. In this paper, we propose a two-step machine learning based framework in which multicentric, heterogeneous images are translated to match the statistical properties of a standard domain. We apply our standardization model to a publicly available multicentric dataset, where we show that we reduce cross-domain while preserving within-domain variability.
Source: HAL
Authors: Hognon, Clément, Tixier, Florent, Gallinato, Olivier, Colin, Thierry, Visvikis, Dimitris, Jaouen, Vincent
Institutions: Laboratoire de Traitement de l'Information Médicale, Inserm
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3001602557