TY - GEN
T1 - Gastric Cancer Diagnosis with Mask R-CNN
AU - Cao, Guitao
AU - Song, Wenli
AU - Zhao, Zhenwei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - It is a reliable way to judge gastric cancer by pathological section. Using deep learning method to detect medical images, as an auxiliary diagnosis method, it can improve the speed and accuracy of doctors to diagnose gastric cancer, and reduce misdiagnosis and missed diagnosis. Mask R-CNN is the latest method in the related field at the beginning of the research. It is mainly used to segment the objects in daily life and achieve good results. The medical image is very different from the scene in life, and the detection effect is also weakened. We use the Mask R-CNN method to detect the pathological sections of gastric cancer, and segment the cancer nest, and then optimize it by adjusting parameters. The method finally allows it to obtain a test result with an AP value of 61.2 when detecting medical images.
AB - It is a reliable way to judge gastric cancer by pathological section. Using deep learning method to detect medical images, as an auxiliary diagnosis method, it can improve the speed and accuracy of doctors to diagnose gastric cancer, and reduce misdiagnosis and missed diagnosis. Mask R-CNN is the latest method in the related field at the beginning of the research. It is mainly used to segment the objects in daily life and achieve good results. The medical image is very different from the scene in life, and the detection effect is also weakened. We use the Mask R-CNN method to detect the pathological sections of gastric cancer, and segment the cancer nest, and then optimize it by adjusting parameters. The method finally allows it to obtain a test result with an AP value of 61.2 when detecting medical images.
KW - Gastric Cancer
KW - Instance Segmentation
KW - Mask R-CNN
KW - Pathological Section
UR - https://www.scopus.com/pages/publications/85078279461
U2 - 10.1109/IHMSC.2019.00022
DO - 10.1109/IHMSC.2019.00022
M3 - 会议稿件
AN - SCOPUS:85078279461
T3 - Proceedings - 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2019
SP - 60
EP - 63
BT - Proceedings - 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2019
Y2 - 24 August 2019 through 25 August 2019
ER -
