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Updated: Jul 7, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

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从胸部X射线进行肺部疾病严重程度分类的深度主动学习:在存在阶级失衡的情况下,用更少的数据学习

Roy M Gabriel1, Mohammadreza Zandehshahvar1, Marly van Assen2

  • 1School of Electrical and Computer Engineering, Georgia Institute of Technology, 791 Atlantic Dr NW, Atlanta, GA, 30332, USA.

Journal of imaging informatics in medicine
|December 10, 2025
PubMed
概括

使用贝叶斯神经网络近似的深度主动学习显著减少了从胸部X射线分类COVID-19肺部疾病严重程度的标记数据. 这种方法保持了高的诊断性能,同时解决了阶级不平衡.

关键词:
贝叶斯神经网络是贝叶斯神经网络.胸部X射线分类 胸部X射线分类医学成像中的阶级不平衡深度主动学习是指深度主动学习.肺部疾病的严重程度肺部疾病的严重程度

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