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.

中文翻译：

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使用一维卷积神经网络对肝细胞癌样品的高光谱数据进行分类和分割。

病理诊断在肝细胞癌（HCC）的诊断和治疗中起着重要作用。大多数癌症的传统病理诊断方法需要冷冻，切片，苏木精和曙红染色以及人工分析，从而限制了诊断过程的速度。在这项研究中，我们设计了一个一维卷积神经网络，对由我们的高光谱成像系统获取的HCC样本切片的高光谱数据进行分类。加权损失函数被用来促进模型的性能。所提出的方法使我们能够加快识别肿瘤组织的诊断过程。我们的深度学习模型在我们的数据集上获得了良好的性能，其灵敏度，特异性和接收器工作特征曲线下的面积分别为0.871、0.888和0.950。同时，我们的深度学习模型优于在我们的数据集上评估的其他机器学习方法。所提出的方法是无标签和实时病理诊断的潜在工具。©2019国际细胞计数学会。