基于深度分割网络和人口统计学附加放射组学算法的综合模型在早期肺腺癌分割和诊断中的开发与评估》[《计算机医学影像与图形学》第 109 (2023) 102299 卷]更正。

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL Computerized Medical Imaging and Graphics Pub Date : 2024-10-02 DOI:10.1016/j.compmedimag.2024.102428
Juyoung Lee, Jaehee Chun, Hojin Kim, Jin Sung Kim, Seong Yong Park
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Corrigendum to 'Development and evaluation of an integrated model based on a deep segmentation network and demography-added radiomics algorithm for segmentation and diagnosis of early lung adenocarcinoma' [Computerized Medical Imaging and Graphics Volume 109 (2023) 102299].
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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