Generative domain adaptation for chest X‐ray image analysis

• Main Authors: Baocai Yin, Wenchao Liu, Zhonghua Fu, Jing Zhang, Cong Liu, Zengfu Wang
• Format: Article
• Language: English
• Published: Wiley 2021-11-01
• Series: IET Image Processing
• Online Access: https://doi.org/10.1049/ipr2.12305

Abstract Chest X‐ray images taken under different conditions follow different distributions, preventing the models trained on a domain from generalising well on the other domain. In this paper, a generative domain adaptation (GDA) method is proposed to address this issue and facilitate the learning process for downstream analysis. GDA adapts different domains to a virtual common one where images are aligned at the appearance level. To this end, a domain shared generator is used to transform the input images and two competitive discriminators are used to adversarially supervise the transforming process. The domain discriminator drives the generator to narrow the domain gap while the fidelity discriminator forces the generator to keep the inherent information. Moreover, a specific classification or detection network is attached to the generator to supervise it in a task‐oriented manner. Experiment results on a large‐scale dataset containing 46k chest X‐ray images demonstrate that GDA outperforms representative domain adaptation methods by a large margin for both disease classification and lesion detection as well as provides useful transformed images to assist experts for diagnosis.