Deep learning and the evaluation of pulmonary fibrosis

David L Levin

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Deep learning and the evaluation of pulmonary fibrosis

机译：深度学习与肺纤维化评价

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来源
《The lancet. Respiratory medicine. 》 |2018年第11期| 共3页
作者
David L Levin;
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作者单位

Mayo College of Medicine Department of Radiology Mayo Clinic;

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原文格式 PDF
正文语种 eng
中图分类呼吸系及胸部疾病 ;
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1. Deep learning and the evaluation of pulmonary fibrosis [J] . David L Levin The lancet. Respiratory medicine. . 2018 ,第11期

机译：深度学习与肺纤维化评价
2. Development of Adverse Outcome Pathway for PPARy Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach [J] . Jaeseong Jeong, Natalia Garcia-Reyero, Lyle Burgoon, Chemical research in toxicology . 2019 ,第6期

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3. A deep learning approach to evaluate intestinal fibrosis in magnetic resonance imaging models [J] . Morilla Ian Neural computing & applications . 2020 ,第18期

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4. Deep Learning Approach for Auto-Detecting Idiopathic Pulmonary Fibrosis Prediction [C] . Ziyuan Wang IEEE International Conference on Artificial Intelligence and Industrial Design . 2021

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5. Adenosine induced pulmonary fibrosis and the contribution of the adenosine A2B receptor to the induction of osteopontin in a mouse model of pulmonary fibrosis [D] . Chunn, Janci Lee 2006

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6. Deep learning and the evaluation of pulmonary fibrosis [O] . David L Levin -1

机译：深度学习与肺纤维化评估
7. Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach [O] . Jaeseong Jeong, Natalia Garcia-Reyero, Lyle Burgoon, 2019

机译：PPARγ拮抗作用的不良结果的发展导致肺纤维化和化学选择的验证：Toxcast数据库与基于深度学习人工神经网络模型的方法

Deep learning and the evaluation of pulmonary fibrosis

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