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Updated: Jun 19, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Published on: December 19, 2020

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通过弱监督学习的胸部X射线图进行解剖学特异性进展分类

Ke Yu1, Shantanu Ghosh1, Zhexiong Liu1

  • 1From the School of Computing and Information, University of Pittsburgh, Pittsburgh, Pa (K.Y., Z.L.); Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St, Office 421, Boston, MA 02215 (S.G., K.B.); Department of Radiology, University of Pittsburgh, Pittsburgh, Pa (C.D.); and Chobanian & Avedisian School of Medicine, Boston University, Boston, Mass (C.B.P.).

Radiology. Artificial intelligence
|July 24, 2024
PubMed
概括

这项研究引入了一种机器学习模型,用于使用放射学报告标签对胸部X射线中疾病进展进行分类. 该模型有效监测间隔变化,并识别新的病理状况.

关键词:
卷积神经网络 (CNN) 是一种神经网络.紧急辐射学 紧急辐射学命名实体的认可方式预测 预后 预测 预测转移学习学习 转移学习没有监督的学习学习.

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