DenseTransXR: A Hybrid Transformer-Based Approach for Chest X-ray Abnormality and Emerging Disease Detection

In this talk, I presented DenseTransXR, a hybrid architecture that combines DenseNet-121 with Transformer encoders to address the shortage of labeled radiography data during the COVID-19 pandemic. The model achieved an AUC of 0.812 on NIH ChestX-ray14 for multi-label abnormality detection and demonstrated strong zero-shot generalization on COVIDx CXR-4 with an AUC of 0.755 and a recall of 0.919 for COVID-19 detection. These results highlight the capability of hybrid Transformer models to improve radiographic diagnosis and handle previously unseen diseases effectively.