Abstract
Goal: Squamous cell carcinoma of cervix is one of the most prevalent cancer worldwide in females. Traditionally, the most indispensable diagnosis of cervix squamous carcinoma is histopathological assessment which is achieved under microscope by pathologist. However, human evaluation of pathology slide is highly depending on the experience of pathologist, thus big inter- and intra-observer variability exists. Digital pathology, in combination with deep learning provides an opportunity to improve the objectivity and efficiency of histopathologic slide analysis. Methods: In this study, we obtained 800 haematoxylin and eosin stained slides from 300 patients suffered from cervix squamous carcinoma. Based on information from morphological heterogeneity in the tumor and its adjacent area, we established deep learning models using popular convolution neural network architectures (inception-v3, InceptionResnet-v2 and Resnet50). Then random forest was introduced to feature extractions and slide-based classification. Results: The overall performance of our proposed models on slide-based tumor discrimination were outstanding with an AUC scores > 0.94. While, location identifications of lesions in whole slide images were mediocre (FROC scores > 0.52) duo to the extreme complexity of tumor tissues. Conclusion: For the first time, our analysis workflow highlighted a quantitative visual-based slide analysis of cervix squamous carcinoma. Significance: This study demonstrates a pathway to assist pathologist and accelerate the diagnosis of patients by utilizing new computational approaches.
Abstract (translated)
目的：子宫颈鳞状细胞癌是世界上女性最常见的癌症之一。传统上，宫颈鳞癌最重要的诊断是病理学家在显微镜下进行的组织病理学评估。然而，人类对病理切片的评估高度依赖于病理学家的经验，因此存在着较大的观察者间和观察者内变异性。数字病理学与深入学习相结合，为提高组织病理学玻片分析的客观性和效率提供了机会。方法：本研究从300例宫颈鳞癌患者中获得800张苏木精和伊红染色片。基于肿瘤及其邻近区域的形态学异质性信息，我们利用流行的卷积神经网络结构（inception-v3、inceptionresnet-v2和resnet50）建立了深度学习模型。然后引入随机森林进行特征提取和基于滑动的分类。结果：我们提出的基于玻片的肿瘤鉴别模型的整体表现显著，AUC评分>0.94。然而，由于肿瘤组织的极端复杂性，在整个玻片图像中病灶的位置识别是平庸的（froc评分>0.52）。结论：我们的分析工作流程首次强调了宫颈鳞癌的定量视觉玻片分析。意义：这项研究证明了一个途径，以协助病理学家和加快诊断病人，利用新的计算方法。
URL
https://arxiv.org/abs/1905.10959
