Shang Lin-Wei, Ma Dan-Ying, Fu Juan-Juan, Lu Yan-Fei, Zhao Yuan, Xu Xin-Yu, Yin Jian-Hua
Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Department of Pathology, Jiangsu Cancer Hospital, Nanjing 210009, China.
Biomed Opt Express. 2020 Jun 9;11(7):3673-3683. doi: 10.1364/BOE.394772. eCollection 2020 Jul 1.
Deep learning is usually combined with a single detection technique in the field of disease diagnosis. This study focused on simultaneously combining deep learning with multiple detection technologies, fluorescence imaging and Raman spectroscopy, for breast cancer diagnosis. A number of fluorescence images and Raman spectra were collected from breast tissue sections of 14 patients. Pseudo-color enhancement algorithm and a convolutional neural network were applied to the fluorescence image processing, so that the discriminant accuracy of test sets, 88.61%, was obtained. Two different BP-neural networks were applied to the Raman spectra that mainly comprised collagen and lipid, so that the discriminant accuracy of 95.33% and 98.67% of test sets were gotten, respectively. Then the discriminant results of fluorescence images and Raman spectra were counted and arranged into a characteristic variable matrix to predict the breast tissue samples with partial least squares (PLS) algorithm. As a result, the predictions of all samples are correct, with minor error of predictive value. This study proves that deep learning algorithms can be applied into multiple diagnostic optics/spectroscopy techniques simultaneously to improve the accuracy in disease diagnosis.
在疾病诊断领域，深度学习通常与单一检测技术相结合。本研究着重于将深度学习与多种检测技术——荧光成像和拉曼光谱——同时结合用于乳腺癌诊断。从14名患者的乳腺组织切片中收集了许多荧光图像和拉曼光谱。将伪彩色增强算法和卷积神经网络应用于荧光图像处理，从而获得了测试集88.61%的判别准确率。将两种不同的BP神经网络应用于主要由胶原蛋白和脂质组成的拉曼光谱，分别获得了测试集95.33%和98.67%的判别准确率。然后对荧光图像和拉曼光谱的判别结果进行计数并整理成特征变量矩阵，用偏最小二乘法（PLS）算法对乳腺组织样本进行预测。结果，所有样本的预测均正确，预测值误差较小。本研究证明深度学习算法可同时应用于多种诊断光学/光谱技术，以提高疾病诊断的准确性。
