Classification and Segmentation of Hyperspectral Data of Hepatocellular Carcinoma Samples Using 1‐D Convolutional Neural Network. Issue 1 (12th August 2019)

• Record Type: Journal Article
• Authors: Wang, Rendong; He, Yida; Yao, Cuiping; Wang, Sijia; Xue, Yuan; Zhang, Zhenxi; Wang, Jing; Liu, Xiaolong
• Other Names: Su Xuantao guestEditor.; Wei Xunbin guestEditor.
• Is Part Of: Cytometry. Volume 97:Issue 1(2020)
• Page Start: 31
• Page End: 38
• Publication Date: 2019-08-12
• Subjects: hyperspectral imaging -- label‐free diagnosis -- computer‐aided diagnosis -- deep learning -- convolutional neural network -- hepatocellular carcinoma; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.23871
• Languages: English
• ISSNs: 1552-4922

Abstract

Abstract: Pathological diagnosis plays an important role in the diagnosis and treatment of hepatocellular carcinoma (HCC). The traditional method of pathological diagnosis of most cancers requires freezing, slicing, hematoxylin and eosin staining, and manual analysis, limiting the speed of the diagnosis process. In this study, we designed a one‐dimensional convolutional neural network to classify the hyperspectral data of HCC sample slices acquired by our hyperspectral imaging system. A weighted loss function was employed to promote the performance of the model. The proposed method allows us to accelerate the diagnosis process of identifying tumor tissues. Our deep learning model achieved good performance on our data set with sensitivity, specificity, and area under receiver operating characteristic curve of 0.871, 0.888, and 0.950, respectively. Meanwhile, our deep learning model outperformed the other machine learning methods assessed on our data set. The proposed method is a potential tool for the label‐free and real‐time pathologic diagnosis. © 2019 International Society for Advancement of Cytometry Abstract : We designed a deep learning method based on one‐dimensional convolutional neural network to classify the hyperspectral data of hepatocellular carcinoma sample slices to determine whether the hyperspectral data indicate tumor tissues. The classified pixels are combined into a complete image. Our method achieves an accuracy of 88.1% and the algorithm only takes few seconds to do the prediction. It is a potential tool for the label‐free and real‐time pathologic diagnosis.