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Deep Adversarial One-Class Learning for Normal and Abnormal Chest Radiograph Classification

文献类型： 会议论文

第一作者： Yu-Xing Tang

作者： Yu-Xing Tang 1 ; You-Bao Tang 1 ; Mei Han 2 ; Jing Xiao 3 ; Ronald M. Summers 1 ;

作者机构： 1.Imaging Biomarkers and Computer-aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, USA

2.Ping An Technology, US Research Lab, USA

3.Ping An Technology Co., Ltd., Shenzhen, China

关键词： Generative Adversarial Networks;One-Class Learning;Anomaly Detection;Chest Radiography

会议名称： Conference on Medical Imaging: Computer-Aided Diagnosis

主办单位：

页码： 1095018-1-1095018-7

摘要： 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.

分类号： R312

  • 相关文献

[1]ABNORMAL CHEST X-RAY IDENTIFICATION WITH GENERATIVE ADVERSARIAL ONE-CLASS CLASSIFIER. Yu-Xing Tang,You-Bao Tang,Mei Han,Jing Xiao,Ronald M. Summers. 2019

[2]Impact of Deepfake Technology on Children: Risks and Consequences. Szandra Anna Laczi,Valéria Póser. 2024

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