  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.
