activity
20202026
most citedSelf-supervised Contrastive Video-Speech Representation Learning for Ultrasound

3 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

SAFIRE: Safety-Critical Benchmark for Fine-grained Fire and Smoke Understanding in Multimodal LLMs

Pengfei Li, Naufal Suryanto, Sicheng Zhang +2

Multimodal Large Language Models (MLLMs) show strong progress on vision-language tasks, yet their reliability in safety-critical settings remains underexplored. Fire-smoke understa…

cs.CV2026

Experience-Guided Self-Adaptive Cascaded Agents for Breast Cancer Screening and Diagnosis with Reduced Biopsy Referrals

Pramit Saha, Mohammad Alsharid, Joshua Strong +1

We propose an experience-guided cascaded multi-agent framework for Breast Ultrasound Screening and Diagnosis, called BUSD-Agent, that aims to reduce diagnostic escalation and unnec…

cs.LG2026

Picking the Right Specialist: Attentive Neural Process-based Selection of Task-Specialized Models as Tools for Agentic Healthcare Systems

Pramit Saha, Joshua Strong, Mohammad Alsharid +2

Task-specialized models form the backbone of agentic healthcare systems, enabling the agents to answer clinical queries across tasks such as disease diagnosis, localization, and re…

cs.CV2023

Show from Tell: Audio-Visual Modelling in Clinical Settings

Jianbo Jiao, Mohammad Alsharid, Lior Drukker +3

Auditory and visual signals usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, the audio-visual modell…

cs.CY2022

An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates

David Johnson, Mohammad Alsharid, Rasheed El-Bouri +5

Teaching artificial intelligence (AI) is challenging. It is a fast moving field and therefore difficult to keep people updated with the state-of-the-art. Educational offerings for…

cs.CV2020★ 3 cited

Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound

Jianbo Jiao, Yifan Cai, Mohammad Alsharid +3

In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a resul…