# Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images

arXiv: https://arxiv.org/abs/1905.10959 · HTML: https://mlsanity.com/paper/1905.10959/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 4 of 4)

- [BMAD: Benchmarks for Medical Anomaly Detection](https://mlsanity.com/paper/2306.11876/) (2023)
- [HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization](https://mlsanity.com/paper/2111.04738/) (2021)
- [Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification](https://mlsanity.com/paper/2306.05029/) (2023)
- [Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data](https://mlsanity.com/paper/2303.10342/) (2023)

## Abstract (arXiv)

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.