activity
20242026
collaborators

11 papers

cs.CV2026

Quran-MD: A Fine-Grained Multilingual Multimodal Dataset of the Quran

Muhammad Umar Salman, Mohammad Areeb Qazi, Mohammed Talha Alam

We present Quran MD, a comprehensive multimodal dataset of the Quran that integrates textual, linguistic, and audio dimensions at the verse and word levels. For each verse (ayah),…

cs.LG2025

Beyond Generative AI: World Models for Clinical Prediction, Counterfactuals, and Planning

Mohammad Areeb Qazi, Maryam Nadeem, Mohammad Yaqub

Healthcare requires AI that is predictive, reliable, and data-efficient. However, recent generative models lack physical foundation and temporal reasoning required for clinical dec…

cs.CV2025

MAFM^3: Modular Adaptation of Foundation Models for Multi-Modal Medical AI

Mohammad Areeb Qazi, Munachiso S Nwadike, Ibrahim Almakky +2

Foundational models are trained on extensive datasets to capture the general trends of a domain. However, in medical imaging, the scarcity of data makes pre-training for every doma…

eess.IV2025

UNICON: UNIfied CONtinual Learning for Medical Foundational Models

Mohammad Areeb Qazi, Munachiso S Nwadike, Ibrahim Almakky +2

Foundational models are trained on extensive datasets to capture the general trends of a domain. However, in medical imaging, the scarcity of data makes pre-training for every doma…

cs.CV2025

MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks

Ibrahim Almakky, Santosh Sanjeev, Anees Ur Rehman Hashmi +3

Transfer learning has become a powerful tool to initialize deep learning models to achieve faster convergence and higher performance. This is especially useful in the medical imagi…

eess.IV2024

XReal: Realistic Anatomy and Pathology-Aware X-ray Generation via Controllable Diffusion Model

Anees Ur Rehman Hashmi, Ibrahim Almakky, Mohammad Areeb Qazi +4

Large-scale generative models have demonstrated impressive capabilities in producing visually compelling images, with increasing applications in medical imaging. However, they cont…