11 papers
Do Medical Vision Language Models Actually See? A Counterfactual Grounding Framework and Hard-Negative Contrastive Training for Visually-Reliant Medical VLMs
Anas Zafar, Leema Krishna Murali, Siddhant Bharadwaj +2
Large vision language models (VLMs) report strong accuracy on medical question-answering, yet it remains unclear whether they reason from visual evidence or exploit textual shortcu…
CARL-CXR: Continual Adapter-Based Routing for Task-Unknown Chest Radiograph Classification
Muthu Subash Kavitha, Anas Zafar, Amgad Muneer +1
Clinical deployment of chest radiograph classifiers requires models that can be updated as new datasets become available without retraining on previously observed data or degrading…
Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks
Benjamin Warner, Ratna Sagari Grandhi, Max Kieffer +32
Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insufficient coverage of relevant…
Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models
Sumra Khan, Sagar Chhabriya, Aizan Zafar +5
Medical vision-language models (VLMs) show strong performance on radiology tasks but often produce fluent yet weakly grounded conclusions due to over-reliance on a dominant modalit…
Projection Guided Personalized Federated Learning for Low Dose CT Denoising
Anas Zafar, Muhammad Waqas, Amgad Muneer +2
Low-dose CT (LDCT) reduces radiation exposure but introduces protocol-dependent noise and artifacts that vary across institutions. While federated learning enables collaborative tr…
Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future
Shaina Raza, Rizwan Qureshi, Anam Zahid +14
Generative AI is rapidly moving from research to deployment, elevating the need for responsible development, evaluation, and governance. We conduct a PRISMA guided review of 232 st…