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Proceedings Paper

Mass classification in mammography with multi-agent based fusion of human and machine intelligence
Author(s): Dongdong Xi; Ming Fan; Lihua Li; Juan Zhang; Yanna Shan; Gang Dai; Bin Zheng
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Paper Abstract

Although the computer-aided diagnosis (CAD) system can be applied for classifying the breast masses, the effects of this method on improvement of the radiologist’ accuracy for distinguishing malignant from benign lesions still remain unclear. This study provided a novel method to classify breast masses by integrating the intelligence of human and machine. In this research, 224 breast masses were selected in mammography from database of DDSM with Breast Imaging Reporting and Data System (BI-RADS) categories. Three observers (a senior and a junior radiologist, as well as a radiology resident) were employed to independently read and classify these masses utilizing the Positive Predictive Values (PPV) for each BI-RADS category. Meanwhile, a CAD system was also implemented for classification of these breast masses between malignant and benign. To combine the decisions from the radiologists and CAD, the fusion method of the Multi-Agent was provided. Significant improvements are observed for the fusion system over solely radiologist or CAD. The area under the receiver operating characteristic curve (AUC) of the fusion system increased by 9.6%, 10.3% and 21% compared to that of radiologists with senior, junior and resident level, respectively. In addition, the AUC of this method based on the fusion of each radiologist and CAD are 3.5%, 3.6% and 3.3% higher than that of CAD alone. Finally, the fusion of the three radiologists with CAD achieved AUC value of 0.957, which was 5.6% larger compared to CAD. Our results indicated that the proposed fusion method has better performance than radiologist or CAD alone.

Paper Details

Date Published: 25 March 2016
PDF: 8 pages
Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 97890R (25 March 2016); doi: 10.1117/12.2225139
Show Author Affiliations
Dongdong Xi, Hangzhou Dianzi Univ. (China)
Ming Fan, Hangzhou Dianzi Univ. (China)
Lihua Li, Hangzhou Dianzi Univ. (China)
Juan Zhang, Zhejiang Cancer Hospital (China)
Yanna Shan, Hangzhou First People’s Hospital (China)
Gang Dai, Zhejiang Cancer Hospital (China)
Bin Zheng, Hangzhou Dianzi Univ. (China)
Univ. of Oklahoma (United States)

Published in SPIE Proceedings Vol. 9789:
Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations
Jianguo Zhang; Tessa S. Cook, Editor(s)

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