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深度学习在原发性肝癌相关诊断模型中的应用与前景

张清华 李海涛 方国旭 郭鹏飞 刘景丰

引用本文:
Citation:

深度学习在原发性肝癌相关诊断模型中的应用与前景

DOI: 10.3969/j.issn.1001-5256.2022.01.003
基金项目: 

福州市科技局科技创新平台项目 (2021-P-055);

福建省发展和改革委员会专项基金 (31010308)

利益冲突声明:所有作者均声明不存在利益冲突。
作者贡献声明:张清华起草并撰写文章主要内容;李海涛负责主要内容修改并完善文章内容;方国旭负责协助查阅文献并提出修改意见;郭鹏飞提供了文章思路;刘景丰参与修改及审校。
详细信息
    通信作者:

    李海涛,lht45182@163.com

    刘景丰,drjingfeng@126.com

Application and prospect of deep learning in primary liver cancer-related diagnostic model

Research funding: 

Science and Technology Innovation Platform Project of Fuzhou Science and Technology Bureau (2021-P-055);

Specific Foundation of Development and Reform Commission in Fujian Province (31010308)

  • 摘要: 深度学习是机器学习通过大量数据训练及分析来模拟人脑的学习行为而获得新的知识和技能。随着医学技术的进步, 医学领域积累了大量的数据, 对数据的研究有助于深入了解数据内的联系与规律, 从而有助于预测人类疾病的发生与预后。深度学习通过找出数据中隐藏的信息, 在医学领域中的应用日益突出。原发性肝癌是发病率和死亡率很高的恶性肿瘤, 预后差, 复发率高, 如何早期诊断、及时治疗、预测复发等一直是研究重点之一。本文从肝癌发生风险预测、术后复发与生存风险预测等方面闸述深度学习在肝癌诊断及复发方面的应用进展。

     

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  • 收稿日期:  2021-10-14
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  • 出版日期:  2022-01-20
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