基于深度学习和组织病理图像的癌症分类研究进展

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。

Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images

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.