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2025年5月2日 17:19 星期五
首页 > 过刊浏览>2024年第33卷第4期 >26-38. DOI:10.15888/j.cnki.csa.009466
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卷积神经网络在肝癌病理图像诊断中的应用综述
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基金项目:

国家自然科学基金面上项目(82174528); 山东省研究生教育优质课程和教学资源库建设项目(SDYKC20047, SDYAL2022041); 教育部产学合作协同育人项目(220606121142949)


Application Review of Convolutional Neural Networks in Pathological Images Diagnosis of Liver Cancer
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    摘要:

    肝癌是一种恶性肝肿瘤, 起源于肝细胞. 肝癌诊断一直是医学难点问题, 也是各领域研究的热点问题, 早期确诊肝癌可以降低肝癌的死亡率. 组织病理学图像检查是肿瘤学诊断的黄金标准, 图像会显示组织切片的细胞和组织结构, 可以用于确定细胞类型、组织结构、异常细胞的数量和形态, 并评估肿瘤具体情况. 本文重点研究了卷积神经网络针对病理图像的肝癌诊断算法, 包括肝肿瘤检测、图像分割以及术前预测这3个方面的应用, 详细阐述了卷积神经网络各算法的设计思路和相关改进目的及方法, 以便为研究人员提供更清晰的参考思路. 总结性分析了卷积神经网络算法在诊断中的优缺点, 并对未来可能的研究热点和相关难点进行了探讨.

    Abstract:

    Liver cancer is a malignant liver tumor that originates from liver cells, and its diagnosis has always been a difficult medical problem and a research hotspot in various fields. Early diagnosis of liver cancer can reduce the mortality rate of liver cancer. Histopathological image examination is the gold standard for oncology diagnosis as the images can display the cells and tissue structures of tissue slices, which can be employed to determine cell types, tissue structures, and the number and morphology of abnormal cells, and evaluate the specific condition of the tumor. This study focuses on the application of convolutional neural networks in liver cancer diagnosis algorithms for pathological images, including liver tumor detection, image segmentation, and preoperative prediction. The design ideas and related improvement goals and methods of each algorithm of convolutional neural networks are elaborated in detail to provide clearer reference ideas for researchers. Additionally, the advantages and disadvantages of convolutional neural network algorithms in diagnosis are summarized and analyzed, with potential research hotspots and related difficulties in the future discussed.

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邵润华,刘静,马金刚,王一凡,陈天真,李明.卷积神经网络在肝癌病理图像诊断中的应用综述.计算机系统应用,2024,33(4):26-38

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  • 收稿日期:2023-10-24
  • 最后修改日期:2023-11-27
  • 在线发布日期: 2024-01-30
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