Unsupervised Adversarial Domain Adaptation for Multi-Label Classification of Chest X-Ray

Abstract

In this paper we address the task of unsupervised domain adaptation for multi-label classification problems with convolutional neural networks. We particularly consider the domain shift in between X-ray data sets. Domain adaptation between different X-ray data sets is especially of practical and clinical importance to guarantee applicability across hospitals and clinics, which may use different machines for image acquisition. In contrast to the usual multi-class setting, in multi-label classification tasks multiple labels can be assigned to an input instance instead of just one label. While most related work focus on domain adaptation for multi-class tasks, we consider the more general case of multi-label classification across domains. We propose an adversarial domain adaptation approach, in which the discriminator is equipped with additional conditional information regarding the current classification output. Our experiments show promising and competitive results on publicly available data sets, compared to state of the art approaches.