The development of label-free non-destructive techniques to be used as diagnostic tools in cancer research is of great importance for improving the quality of life for millions of patients. Previous studies have demonstrated that Third Harmonic Generation (THG) imaging could differentiate malignant from benign unlabeled human breast biopsies and distinguish the different grades of cancer. Towards the application of such technologies to clinic, in the present report, a deep learning technique was applied to THG images recorded from breast cancer tissues of grades 0, I, II, and III. By the implementation of a convolutional neural network (CNN) model, the differentiation of malignant from benign breast tissue samples and the discrimination of the different grades of cancer in a fast and accurate way were achieved. The obtained results provide a step ahead towards the use of optical diagnostic tools in conjunction with the CNN image classifier for the reliable and rapid malignancy diagnosis in clinic.

中文翻译：

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深度学习技术在人体组织活检非线性图像中的应用为乳腺癌诊断提供新思路


开发用作癌症研究诊断工具的无标记无损技术对于改善数百万患者的生活质量具有重要意义。先前的研究表明，三次谐波产生（THG）成像可以区分恶性与良性未标记的人类乳腺活检，并区分不同级别的癌症。为了将此类技术应用于临床，在本报告中，将深度学习技术应用于从 0、I、II 和 III 级乳腺癌组织记录的 THG 图像。通过卷积神经网络（CNN）模型的实现，实现了快速准确地区分恶性与良性乳腺组织样本以及区分不同级别的癌症。所获得的结果为将光学诊断工具与 CNN 图像分类器结合使用在临床上实现可靠、快速的恶性肿瘤诊断迈出了一步。