Domain-Invariant Classification of Misplaced Medical Devices in Plain Chest X-Ray Images

Abstract

Chest X-ray imaging is crucial for verifying the correct placement of medical devices. However, deep learning models often fail to generalize across institutions due to domain shifts caused by variations in imaging protocols and annotation practices. This work addresses the challenge of classification of misplaced medical devices in plain chest X-ray images under domain shift by integrating domain adaptation into the training process. It proposes a framework that combines a Vision Transformer architecture with Deep Adaptation Networks, using the Maximum Mean Discrepancy loss to align the feature distributions between a large public dataset and a smaller, clinically distinct private dataset. Our results demonstrate that domain adaptation enables the detection of previously undetected misplacements in the target domain and enhances performance on the source domain, suggesting improved feature generalizability. The baseline model achieved high accuracy but failed to identify true positives in the target domain. In contrast, despite having lower precision, the adapted model increased the number of true positives from zero to twelve.