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Early-to-Late Prediction of DCE-MRI Contrast-Enhanced Images in Using Generative Adversarial Networks

We consider the problem of predicting early-to-late Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) in breast cancer sequences. This is approached with conditional generative adversarial networks that synthesize the late response image given the early response. We propose a novel loss...

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Bibliographic Details
Main Authors: Fonnegra, Ruben D., Liliana Hernandez, Maria, Caicedo, Juan C., Diaz, Gloria M.
Format: Conference Proceeding
Language:English
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Summary:We consider the problem of predicting early-to-late Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) in breast cancer sequences. This is approached with conditional generative adversarial networks that synthesize the late response image given the early response. We propose a novel loss function to improve the ability of GAN models to learn the relevant temporal tissue dynamics under this setting, as well as a clinically relevant metric to assess performance. Our experiments show that the proposed strategy predicts accurate responses and could serve as a solution to implement fast diagnostic protocols.
ISSN:1945-8452
DOI:10.1109/ISBI53787.2023.10230509