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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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Main Authors: | , , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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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. |
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ISSN: | 1945-8452 |
DOI: | 10.1109/ISBI53787.2023.10230509 |