Paper Info
Title
Improving Prostate Cancer (PCa) Classification Performance by Using Three-Player Minimax Game to Reduce Data Source Heterogeneity
Abstract
PCa is a disease with a wide range of tissue patterns and this adds to its classification difficulty. Moreover, the data source heterogeneity, i.e. inconsistent data collected using different machines, under different conditions, by different operators, from patients of different ethnic groups, etc., further hinders the effectiveness of training a generalized PCa classifier. In this paper, for the first time, a Generative Adversarial Network (GAN)-based three-player minimax game framework is used to tackle data source heterogeneity and to improve PCa classification performance, where a proposed modified U-Net is used as the encoder. Our dataset consists of novel high-frequency ExactVu ultrasound (US) data collected from 693 patients at five data centers. Gleason Scores (GSs) are assigned to the 12 prostatic regions of each patient. Two classification tasks: benign vs. malignant and low- vs. high-grade, are conducted and the classification results of different prostatic regions are compared. For benign vs. malignant classification, the three-player minimax game framework achieves an Area Under the Receiver Operating Characteristic (AUC) of 93.4%, a sensitivity of 95.1% and a specificity of 87.7%, respectively, representing significant improvements of 5.0%, 3.9%, and 6.0% compared to those of using heterogeneous data, which confirms its effectiveness in terms of PCa classification.
Year
DOI
Venue
2020
10.1109/TMI.2020.2988198
IEEE Transactions on Medical Imaging
Keywords
DocType
Volume
Principal component analysis,Feature extraction,Biopsy,Cancer,Games,Gallium nitride,Radio frequency
Journal
39
Issue
ISSN
Citations 
10
0278-0062
1
PageRank 
References 
Authors
0.37
0
4
Name
Order
Citations
PageRank
Yanan Shao110.37
Jane Wang210.37
Brian Wodlinger311.72
S. E. Salcudean4920123.70