Cycle-Consistent Adversarial chest X-rays Domain Adaptation for pneumonia diagnosis

Abstract

To deal with the challenge of limited annotated data for training target domain pneumonia classifier, domain adaptation methods leverage publicly available source dataset to enhance performance on the target domain. However, the existing methods lack effective feature representation to ensure a significant similarity between the source domain and the target domain. To address this problem, we propose a novel method, called Cycle-Consistent Adversarial chest X-rays Domain Adaptation (C2ADA) to diagnose pneumonia from Chest X-rays automatically, which can transfer knowledge from a publicly available large-scale source dataset to the small-scale target dataset and achieve high performance with fewer target domain samples. Specifically, C2ADA introduces the cycle-consistent adversarial module to enforce the feature extractor to learn domain-invariant features. Furthermore, unlike most existing domain adaptation methods that deal with the same tasks in the source domain and target domain, our perspective is that heterogeneous tasks can assist the target domain in learning more effectively. Therefore, C2ADA includes two subnetworks, one is for the source domain, which contains a multilabel classification task, and the other is for the target domain, which focuses on the binary classification task. To assess the effectiveness of our method, we conduct a comparative analysis with other domain adaptation approaches. The experimental results show that the proposed method can achieve 96.86% accuracy on the ChestXRay2017 dataset utilizing just 50 images.