13 citations · 25 across the 9 of their papers we have counts for
8 papers · 1 filter
What to Preserve, Where to Adapt: A Depth-Wise Analysis of Forgetting in Continual Gynecological Image Segmentation
Amal Saqib, Tausifa Jan Saleem, Numan Saeed +1
The clinical management of gynecological diseases often relies on medical imaging for diagnosis, treatment planning, and follow-up. Segmentation in this setting is challenging beca…
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…
MedSPOT: A Workflow-Aware Sequential Grounding Benchmark for Clinical GUI
Rozain Shakeel, Abdul Rahman Mohammad Ali, Muneeb Mushtaq +2
Despite the rapid progress of Multimodal Large Language Models (MLLMs), their ability to perform reliable visual grounding in high-stakes clinical software environments remains und…
DuPLUS: Dual-Prompt Vision-Language Framework for Universal Medical Image Segmentation and Prognosis
Numan Saeed, Tausifa Jan Saleem, Fadillah Maani +3
Deep learning for medical imaging is hampered by task-specific models that lack generalizability and prognostic capabilities, while existing 'universal' approaches suffer from simp…
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…
A Comprehensive Review of Knowledge Distillation in Computer Vision
Gousia Habib, Tausifa jan Saleem, Sheikh Musa Kaleem +2
Deep learning techniques have been demonstrated to surpass preceding cutting-edge machine learning techniques in recent years, with computer vision being one of the most prominent…