Evaluation of mammography calcification detection system based on deep learning
10.3760/cma.j.issn.1005-1201.2019.11.008
- VernacularTitle: 基于深度学习的乳腺X线摄影钙化检出系统评估
- Author:
Juan ZHOU
1
;
Tingting WANG
1
;
Ming LI
1
;
Jianxiu ZHAO
1
;
Ping SHUANG
1
;
Fugeng SHENG
1
Author Information
1. Department of Radiology, the Fifth Medical Centre, Southern District of Chinese PLA General Hospital, Beijing 100071,China
- Publication Type:Journal Article
- Keywords:
Mammography;
Suspicious calcification;
Detection;
Deep learning
- From:
Chinese Journal of Radiology
2019;53(11):968-973
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the performance of a deep learning (DL) based mammogram calcification detection system.
Methods:Screening digital mammographic examinations with standard cranio-caudal (CC) and medio-lateral oblique (MLO) views were performed in 1 431 women (5 488 mammogram images) who were enrolled between January and December in 2013. The DL system and a radiologist detect calcifications separately, and then both results are reviewed by a moreexperiencedradiologist. Sensitivities of the DL model and radiologist were compared. Different calcification morphology, distribution, BI-RADS categories, breast density and patient age were investigated by χ2 tests.
Results:For DL system, sensitivity of all kinds of calcifications were 96.76% (7 649/7 905). The average false positive was 1.04 per image (5 706/5 488), 3.99 per case (5 706/1 431). The false positive rate was 42.73% (5 706/13 355). There was no significant differences for DL system with different calcification distribution, BI-RADS categories, breast densities and patient ages (P>0.05).
Conclusion:Deep learning based mammogram calcification detection system shows high sensitivity and stability, which may help to reduce the missing rate of calcification (especially the suspicious ones).
