collaborators

10 papers

cs.CV2026

On the Robustness of Temporal Vision-Language Models for Surgical Endoscopy Videos

Darakshan Rashid, Raza Imam, Ufaq Khan +9

Temporal vision-language models (TVLMs) offer a reusable, prompt-based interface for surgical video understanding, yet, their robustness under clinically realistic acquisition arti…

cs.CV2026

PathWISE: Multi-Agent Cancer Pathway Triaging Ontology Learning from Clinical Flowcharts

Sofiat Abioye, Ufaq Khan, Shazad Ashraf +6

Clinical pathways are disseminated as visual flowcharts where spatial topology, arrow direction, colour coding, and font weight encode critical triage logic that remains inaccessib…

cs.CV2026

RAPTOR+: A Visually Grounded Vision-Language Framework to Improve Clinical Trust and Auditability in Automated Cancer Referral Processing

Sofiat Abioye, Ufaq Khan, Shazad Ashraf +4

Urgent suspected colorectal cancer (CRC) referrals create operational bottlenecks because semi-structured clinical documents often require manual review and transcription. The orig…

cs.CL2026

Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs

Mahdi Alkaeed, Adnan Qayyum, Nabeel Abo Kashreef +2

Large Language Models (LLMs) are increasingly used in clinical applications. However, their behavior remains highly sensitive to subtle linguistic variations, such as rephrasing or…

cs.CV2026

MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding

Basit Alawode, Arif Mahmood, Muaz Khalifa Al-Radi +6

Whole Slide Images (WSIs) exhibit hierarchical structure, where diagnostic information emerges from cellular morphology, regional tissue organization, and global context. Existing…

cs.CV2026

MedObvious: Exposing the Medical Moravec's Paradox in VLMs via Clinical Triage

Ufaq Khan, Umair Nawaz, L D M S S Teja +5

Vision Language Models (VLMs) are increasingly used for tasks like medical report generation and visual question answering. However, fluent diagnostic text does not guarantee safe…