48 citations · 62 across the 10 of their papers we have counts for
7 papers · 1 filter
An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering
Mai A. Shaaban, Tausifa Jan Saleem, Alaa Mohamed +3
Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without forgetting previously acquired…
AURORA: Adaptive Unified Representation for Robust Ultrasound Analysis
Ufaq Khan, L. D. M. S. Sai Teja, Ayuba Shakiru +4
Ultrasound images vary widely across scanners, operators, and anatomical targets, which often causes models trained in one setting to generalize poorly to new hospitals and clinica…
MOTOR: Multimodal Optimal Transport via Grounded Retrieval in Medical Visual Question Answering
Mai A. Shaaban, Tausifa Jan Saleem, Vijay Ram Papineni +1
Medical visual question answering (MedVQA) plays a vital role in clinical decision-making by providing contextually rich answers to image-based queries. Although vision-language mo…
TactileNet: Bridging the Accessibility Gap with AI-Generated Tactile Graphics for Individuals with Vision Impairment
Adnan Khan, Alireza Choubineh, Mai A. Shaaban +2
Tactile graphics are essential for providing access to visual information for the 43 million people globally living with vision loss. Traditional methods for creating these graphic…
MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis
Mai A. Shaaban, Adnan Khan, Mohammad Yaqub
Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions, but efforts to integrate them with structured clinical data face challenges due to…
Improving Pseudo-labelling and Enhancing Robustness for Semi-Supervised Domain Generalization
Adnan Khan, Mai A. Shaaban, Muhammad Haris Khan
Beyond attaining domain generalization (DG), visual recognition models should also be data-efficient during learning by leveraging limited labels. We study the problem of Semi-Supe…
PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis
Santosh Sanjeev, Salwa K. Al Khatib, Mai A. Shaaban +3
Previous deep learning efforts have focused on improving the performance of Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using Convolutional Neural Networks…