Gastric cancer is one of the most common cancers, which ranks third among the leading causes of cancer death. Biopsy of gastric mucosa is a standard procedure in gastric cancer screening test. However, manual pathological inspection is labor-intensive and time-consuming. Besides, it is challenging for an automated algorithm to locate the small lesion regions in the gigapixel whole-slide image and make the decision correctly.To tackle these issues, we collected large-scale whole-slide image dataset with detailed lesion region annotation and designed a whole-slide image analyzing framework consisting of 3 networks which could not only determine the screening result but also present the suspicious areas to the pathologist for reference. Experiments demonstrated that our proposed framework achieves sensitivity of 97.05% and specificity of 92.72% in screening task and Dice coefficient of 0.8331 in segmentation task. Furthermore, we tested our best model in real-world scenario on 10,315 whole-slide images collected from 4 medical centers.
@article{arxiv.1910.03729,
title = {Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network},
author = {Hong Yu and Xiaofan Zhang and Lingjun Song and Liren Jiang and Xiaodi Huang and Wen Chen and Chenbin Zhang and Jiahui Li and Jiji Yang and Zhiqiang Hu and Qi Duan and Wanyuan Chen and Xianglei He and Jinshuang Fan and Weihai Jiang and Li Zhang and Chengmin Qiu and Minmin Gu and Weiwei Sun and Yangqiong Zhang and Guangyin Peng and Weiwei Shen and Guohui Fu},
journal= {arXiv preprint arXiv:1910.03729},
year = {2020}
}