Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images
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摘要: 癌症的精确分类直接关系到患者治疗方案的选择和预后。病理诊断是癌症诊断的金标准,病理图像的数字化和深度学习的突破性进展使得计算机辅助癌症诊断和预后预测成为可能。本文通过简述病理图像分类常用的4种深度学习方法,总结基于深度学习和组织病理图像的癌症分类最新研究进展,指出该领域研究中普遍存在的问题与挑战,并对未来可能的发展方向进行展望。Abstract: Accurate classification of cancer is directly related to the choice of treatment options and prognosis. Pathological diagnosis is the gold standard for cancer diagnosis. The digitalization of pathological images and breakthroughs in deep learning have made computer-aided diagnosis and prediction about prognosis possible. In this paper, we first briefly describe four deep learning methods commonly used in this field, and then review the latest research progress in cancer classification based on deep learning and histopathological images. Finally, the general problems in this field are summarized, and the possible development direction in the future is suggested.
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Key words:
- pathological images /
- deep learning /
- cancer classification /
- cancer grading /
- computer-aided diagnosis
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图 3 基于深度学习的病理图像(Image)分类方法的典型框架[28]
图 4 基于深度学习的病理图像(WSI)分类方法的典型框架[30]
表 1 4种常用的深度学习方法的临床应用
病理图像 深度学习方法 方法类型 临床应用领域 Patch CNN 监督学习 乳腺癌[20-22] Image CNN 监督学习 乳腺癌[23-26],结/直肠癌[27] CNN+RNN 监督学习 乳腺癌[28] CNN+GCN 监督学习 结/直肠癌[29] WSI CNN 监督学习 肺癌[30], 前列腺癌[31], 结肠癌[32] CNN+MIL 弱监督学习 胃癌[33], 结肠癌[34] CNN+GCN+MIL 弱监督学习 结/直肠癌[35] CNN+MIL+RNN 弱监督学习 前列腺癌, 皮肤癌和腋窝淋巴结[36] CNN:卷积神经网络;RNN:循环神经网络;GCN:图卷积神经网络;MIL:多示例学习 -
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