鼻咽CT图像金属伪影减少的改进对比非配对平移

2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI:10.1109/CAI54212.2023.00152

Yu-Hsing Hsieh, Jia-Da Li, Yao Lee, Chu-Song Chen, LiFu Wu, S. H. Cheng

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引用次数: 0

摘要

金属伪影(MA)还原是临床应用的关键，但往往缺乏成对的训练数据。从非配对数据中学习MA约简和增强保真度似乎是一种权衡。本研究提出了一种改进的对比非配对翻译方案来解决这些问题并证明其有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Improved Contrastive Unpaired Translation for Metal Artifacts Reduction in Nasopharyngeal CT Images

Metal artifacts (MA) reduction is crucial for clinical application yet often lacks paired training data. Learning MA reduction from unpaired data and enforcing fidelity seems a trade-off. The study proposed an improved contrastive unpaired translation solution to address the issues and demonstrate its efficacy.

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来源期刊

2023 IEEE Conference on Artificial Intelligence (CAI)

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