Standardization of Multicentric Image Datasets with Generative Adversarial Networks
Résumé
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.
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