Denoising Low-Dose CT Images Using Noise2Noise and Evaluation of Hyperparameters

被引：0
|
作者：
Man, Or [1 ]
Cohen, Miri Weiss [1 ]
机构：
[1] Braude Coll Engn, Karmiel, Israel
来源：
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2023, PT I | 2023年 / 14134卷
关键词：
CT scans; Noise2Noise; U-Net; Hyper-parameter; Optimization; NETWORK;
D O I：
10.1007/978-3-031-43085-5_34
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In computed tomography (CT), the quality of the image is directly related to the exposure of the patient during the scan. A reduction in exposure reduces the health risks for patients, however, an increase in noise compromises the image quality. This work examines the Noise2Noise framework, which requires only noisy image pairs for network training in order to minimize the noise in CT images. This study examines the effects of varying learning rates, batch sizes, epochs, and encoder-decoder network depths on a variety of loss functions and their parameters.
引用
收藏
页码：433 / 447
页数：15
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