  Background: Chest X-ray imaging-based abnormality localization, essential in
diagnosing various diseases, faces significant clinical challenges due to
complex interpretations and the growing workload of radiologists. While recent
advances in deep learning offer promising solutions, there is still a critical
issue of domain inconsistency in cross-domain transfer learning, which hampers
the efficiency and accuracy of diagnostic processes. This study aims to address
the domain inconsistency problem and improve autonomic abnormality localization
performance of heterogeneous chest X-ray image analysis, by developing a
self-supervised learning strategy called "BarlwoTwins-CXR". Methods: We
utilized two publicly available datasets: the NIH Chest X-ray Dataset and the
VinDr-CXR. The BarlowTwins-CXR approach was conducted in a two-stage training
process. Initially, self-supervised pre-training was performed using an
adjusted Barlow Twins algorithm on the NIH dataset with a Resnet50 backbone
pre-trained on ImageNet. This was followed by supervised fine-tuning on the
VinDr-CXR dataset using Faster R-CNN with Feature Pyramid Network (FPN).
Results: Our experiments showed a significant improvement in model performance
with BarlowTwins-CXR. The approach achieved a 3% increase in mAP50 accuracy
compared to traditional ImageNet pre-trained models. In addition, the Ablation
CAM method revealed enhanced precision in localizing chest abnormalities.
Conclusion: BarlowTwins-CXR significantly enhances the efficiency and accuracy
of chest X-ray image-based abnormality localization, outperforming traditional
transfer learning methods and effectively overcoming domain inconsistency in
cross-domain scenarios. Our experiment results demonstrate the potential of
using self-supervised learning to improve the generalizability of models in
medical settings with limited amounts of heterogeneous data.
