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- W4386362766 abstract "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." @default.
- W4386362766 created "2023-09-02" @default.
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- W4386362766 date "2023-04-18" @default.
- W4386362766 modified "2023-09-27" @default.
- W4386362766 title "Early-to-Late Prediction of DCE-MRI Contrast-Enhanced Images in Using Generative Adversarial Networks" @default.
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- W4386362766 doi "https://doi.org/10.1109/isbi53787.2023.10230509" @default.
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