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

引用次数: 0

摘要

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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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

Computerized Medical Imaging and Graphics 医学-核医学

CiteScore

10.70

自引率

3.50%

发文量

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.