1. Large-scale gastric cancer screening and localization using multi-task deep neural network
- Author
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Wen Chen, Jiji Yang, Wanyuan Chen, Chengmin Qiu, Yangqiong Zhang, Li Zhang, Zhiqiang Hu, Liren Jiang, Chenbin Zhang, Xiaofan Zhang, Qi Duan, Xianglei He, Xiaodi Huang, Guo-Hui Fu, Guangyin Peng, Weiwei Sun, Minmin Gu, Weihai Jiang, Hong Yu, Jiahui Li, Jinshuang Fan, Wei-Wei Shen, and Ling-Jun Song
- Subjects
0209 industrial biotechnology ,medicine.diagnostic_test ,Artificial neural network ,Computer science ,business.industry ,Cognitive Neuroscience ,Cancer ,Pattern recognition ,02 engineering and technology ,medicine.disease ,Computer Science Applications ,Task (project management) ,Causes of cancer ,020901 industrial engineering & automation ,Sørensen–Dice coefficient ,Artificial Intelligence ,Biopsy ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,020201 artificial intelligence & image processing ,Segmentation ,Artificial intelligence ,business ,Scale (map) - Abstract
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.
- Published
- 2021
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