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Updated: Aug 1, 2026

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深度因果学习用于CT序列中的胰腺癌细分

Chengkang Li1, Yishen Mao2, Shuyu Liang1

  • 1School of Information Science and Technology of Fudan University, Shanghai 200433, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200032, China.

Neural networks : the official journal of the International Neural Network Society
|April 24, 2024
PubMed
概括

这项研究介绍了CausegNet,这是一种深度因果学习框架,可以在3DCT扫描中同时对胰腺和瘤进行细分. 它通过减少背景干扰来显著提高胰腺癌的诊断准确性.

关键词:
反事实推断的推断反事实推断.深度因果学习是一种深度因果学习.胰腺癌的共同细分 胰腺癌的共同细分胰腺癌风险评估 胰腺癌风险评估

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