13 citations · 25 across the 9 of their papers we have counts for
13 papers
Multimodal Medical Image Parsing with Task-Specific Low-Rank Adapters
Taha Sabir, Alberto Montero Molina, Thinh Quang Ly +3
Adapting a single vision-language model to medical image parsing requires training choices that reflect the demands of each task. We study task-specific adaptation with Qwen3.5-9B…
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…
Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging
Nazish Khalid, Tausifa Jan Saleem, Amal Saqib +2
Model pruning is widely used to compress deep neural networks, reducing memory and computational requirements with minimal impact on aggregate performance. However, its effect on m…
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…
Deep Learning-Based Automated Segmentation of Uterine Myomas
Tausifa Jan Saleem, Mohammad Yaqub
Uterine fibroids (myomas) are the most common benign tumors of the female reproductive system, particularly among women of childbearing age. With a prevalence exceeding 70%, they p…
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…
FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
Fadillah Maani, Numan Saeed, Tausifa Saleem +8
Foundation models are becoming increasingly effective in the medical domain, offering pre-trained models on large datasets that can be readily adapted for downstream tasks. Despite…
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…
Insights into the Lottery Ticket Hypothesis and Iterative Magnitude Pruning
Tausifa Jan Saleem, Ramanjit Ahuja, Surendra Prasad +1
Lottery ticket hypothesis for deep neural networks emphasizes the importance of initialization used to re-train the sparser networks obtained using the iterative magnitude pruning…
LIB-KD: Teaching Inductive Bias for Efficient Vision Transformer Distillation and Compression
Gousia Habib, Tausifa Jan Saleem, Ishfaq Ahmad Malik +1
With the rapid development of computer vision, Vision Transformers (ViTs) offer the tantalising prospect of unified information processing across visual and textual domains due to…
Knowledge Distillation in Vision Transformers: A Critical Review
Gousia Habib, Tausifa Jan Saleem, Brejesh Lall
In Natural Language Processing (NLP), Transformers have already revolutionized the field by utilizing an attention-based encoder-decoder model. Recently, some pioneering works have…