Share Email Print
cover

Proceedings Paper • new

Deep adversarial one-class learning for normal and abnormal chest radiograph classification
Author(s): Yu-Xing Tang; You-Bao Tang; Mei Han; Jing Xiao; Ronald M. Summers
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In machine learning, one-class classification tries to classify data of a specific category amongst all data, by learning from a training set containing only the data of that unique category. In the field of medical imaging, one-class learning can be developed to model only normality (similar to semi-supervised classification or anomaly detection), since the samples of all possible abnormalities are not always available, as some forms of anomaly are very rare. The one-class learning approach can be naturally adapted to the way radiologists identify anomalies in medical images: usually they are able to recognize lesions by comparing them with normal images and surroundings. Inspired by the traditional one-class learning approach, we propose an end-to-end deep adversarial one-class learning (DAOL) approach for semi-supervised normal and abnormal chest radiograph (X-ray) classification, by training only from normal X-ray images. The DAOL framework consists of deep convolutional generative adversarial networks (DCGAN) and an encoder at each end of the DCGAN. The DAOL generator is able to reconstruct the normal X-ray images while not adequate for well reconstructing the abnormalities in abnormal X-rays in the testing phase, since only the normal X-rays were used for training the network, and the abnormal images with various abnormalities were unseen during training. We propose three adversarial learning objectives which optimize the training of DAOL. The proposed network achieves an encouraging result (AUC 0.805) in classifying normal and abnormal chest X-rays on the challenging NIH Chest X-ray dataset in a semi-supervised setting.

Paper Details

Date Published: 13 March 2019
PDF: 7 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095018 (13 March 2019); doi: 10.1117/12.2511787
Show Author Affiliations
Yu-Xing Tang, National Institutes of Health Clinical Ctr. (United States)
You-Bao Tang, National Institutes of Health Clincal Ctr. (United States)
Mei Han, Ping An Technology, US Research Lab (United States)
Jing Xiao, Ping An Technology Co., Ltd. (China)
Ronald M. Summers, National Institutes of Health Clinical Ctr. (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

© SPIE. Terms of Use
Back to Top