Assessing tuberculosis detection and treatment outcome prediction in x-ray images: a cross-domain foundation model performance analysis

This study evaluates the performance of several pre-trained foundation models (FMs) in diagnosing tuberculosis (TB) using X-ray images. We compared three FMs trained on ImageNet, X-ray images, and 3D CT images using three publicly available TB diagnostic X-ray image collections: TBCXRay, TBX11K, and Shenzhen-Montgomery. The models extracted deep features which were then used to train a Naive Bayes classifier for TB diagnosis. The 3D CT FM achieved the highest average AUC of 0.93 on the TBCXRay validation set, while the ImageNet FM followed closely with an AUC of 0.92. However, both 3D CT and X-ray FMs exhibited higher sensitivity to batch effects, with significantly lower AUCs on the TBX11K and Shenzhen-Montgomery collections compared to the ImageNet FM. These results underscore the effectiveness of out-of-domain FMs in characterizing TB while highlighting the challenges posed by batch effects in medical imaging.