概化理论
计算机科学
标准化
扫描仪
人工智能
计算机视觉
迭代重建
深度学习
对抗制
图像(数学)
数学
统计
操作系统
作者
Md. Selim,Jie Zhang,Baowei Fei,Matthew A. Lewis,Guoqiang Zhang,Jin Chen
标识
DOI:10.1109/bibm55620.2022.9995168
摘要
Large-scale CT image studies often suffer from a lack of homogeneity regarding radiomic characteristics due to the images acquired with scanners from different vendors or with different reconstruction algorithms. We propose a deep learning-based framework called UDA-CT to tackle the homogeneity issue by leveraging both paired and unpaired images. Using UDA-CT, the CT images can be standardized both from different acquisition protocols of the same scanner and CT images acquired using a similar protocol but scanners from different vendors. UDA-CT incorporates recent advances in deep learning including domain adaptation and adversarial augmentation. It includes a unique design for model training batch which integrates nonstandard images and their adversarial variations to enhance model generalizability. The experimental results show that UDA-CT significantly improves the performance of the cross-scanner image standardization by utilizing both paired and unpaired data.
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