3 citations · 8 across the 18 of their papers we have counts for
5 papers · 1 filter
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…
SALT: Parameter-Efficient Fine-Tuning via Singular Value Adaptation with Low-Rank Transformation
Abdelrahman Elsayed, Sarim Hashmi, Mohammed Elseiagy +3
The complex nature of medical image segmentation calls for models that are specifically designed to capture detailed, domain-specific features. Large foundation models offer consid…
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…
Multi-Task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans
Mohammad Areeb Qazi, Mohammed Talha Alam, Ibrahim Almakky +3
Precise estimation of fetal biometry parameters from ultrasound images is vital for evaluating fetal growth, monitoring health, and identifying potential complications reliably. Ho…
Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays
Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarette +2
Quick and accurate diagnosis is of paramount importance to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developin…