Shadi Albarqouni

Improving diagnosis and interpretability in chest X-rays through federated learning and adversarial training

Abstract Background Chest X-ray imaging is the most common radiological test for diagnosing a wide range of lung conditions. Deep Learning (DL) has emerged as a powerful tool for automated analysis of chest X-rays, but it requires large, well-annotated datasets that are often difficult to obtain due to privacy concerns...

Reducing Clinician Annotation Fatigue in Open-Set Federated Learning: A Client-Adaptive Vision-Language Gatekeeper.

PURPOSE Federated learning enables breast-imaging sites to jointly train mammography artificial intelligence (AI) without sharing images, but radiologists at each site must still annotate selected images during active-learning rounds. We developed and evaluated a client-adaptive vision-language gatekeeper that withhold...

K-MaT: Knowledge-Anchored Manifold Transport for Cross-Modal Prompt Learning in Medical Imaging