Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network
| Autor: | Yu, Hong, Zhang, Xiaofan, Song, Lingjun, Jiang, Liren, Huang, Xiaodi, Chen, Wen, Zhang, Chenbin, Li, Jiahui, Yang, Jiji, Hu, Zhiqiang, Duan, Qi, Chen, Wanyuan, He, Xianglei, Fan, Jinshuang, Jiang, Weihai, Zhang, Li, Qiu, Chengmin, Gu, Minmin, Sun, Weiwei, Zhang, Yangqiong, Peng, Guangyin, Shen, Weiwei, Fu, Guohui |
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| Rok vydání: | 2019 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: under minor revision |
| Databáze: | arXiv |
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