Abstract
Generative Adversarial Networks (GAN) are more and more gaining attention in the computer vision domain thanks to their ability to generate synthetic data, in particular in the context of domain adaptation and image-to-image translation. These properties are attracting the medical community too, in order to solve some complex biomedical challenges, such as the translation between different medical imaging acquisition protocols. Indeed, as the actual acquisition protocol is strongly dependent on factors such as the operator, the aim, the centre, etc., gathering cohorts of patients all sharing the same typology of imaging is an open challenge. In this paper, we propose to face this problem by using a GAN to realise a domain translation architecture in the case of breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI), considering two different acquisition protocols, in the context of automatic lesion classification. Despite this work wanting to be a first step toward artificial data generation in the medical domain, the obtained results have been analysed from both a quantitative and qualitative point of view, in order to evaluate the correctness and quality of the proposed architecture as well as its usability in a clinical scenario.
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Galli, A., Gravina, M., Marrone, S., Sansone, C. (2023). Generative Adversarial Networks for Domain Translation in Unpaired Breast DCE-MRI Datasets. In: Conte, D., Fred, A., Gusikhin, O., Sansone, C. (eds) Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science, vol 1875. Springer, Cham. https://doi.org/10.1007/978-3-031-39059-3_25
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