Welcome to the UltraVision+ Lab

Welcome to UltraVision+ Lab at the Department of Computer Science of Hong Kong Baptist University (HKBU). We are a dynamic research group dedicated to advancing medical imaging through the integration of ultrasound with a broad range of modal data, including video, image, audio, text reports, probe motion, eye gaze, and other inputs. The name “UltraVision+” signifies our commitment to not only enhancing visual ultrasound imaging with advanced computer vision but also leveraging additional data streams to provide a comprehensive and enriched diagnostic experience. Our goal is to develop cutting-edge tools and algorithms that improve diagnostic accuracy, streamline procedures, and guide sonographers with real-time insights from diverse data sources. At UltraVision+, we are at the forefront of innovation, driving the future of medical imaging by integrating and interpreting multiple modalities to enhance both the precision and accessibility of ultrasound technology (see Research).

Research Area

  • Multimodal learning including video, image, audio, text, etc.

  • Human-machine interaction or collaboration to simplify sonography.

  • 2D, video and 3D ultrasound data understanding.

  • Data-centric AI to handle imperfect data problems in real-word deployment.

We are looking for passionate PhD, RA, and Intern students to join the team (more info) !

News

Jun 2025

Honored to receive 2025/26 RGC ECS Grant.

Jun 2025

One paper accepted by ICCV 2025. Congrats to Weihao!

Jun 2025

Our paper Sonomate accepted by Nature Biomedical Engineering! Thanks to all co-authors!

Apr 2025

We will host two workshops in MICCAI 2025 (1) Human-AI Collaboration (HAIC). (2) Multimodal Large Language Models (MLLMs) in Clinical Practice.

Apr 2025

One paper accepted by EMBC 2025. Congrats to Chen!

Feb 2025

One paper accepted by IPMI 2025. Congrats to Weihao!

Jan 2025

I am invited to be the Area Chair for MICCAI 2025!

Oct 2024

I was invited as a Guest Editor of the Special Issue “Computer Vision Powered Human-Machine Interaction in Healthcare” for Sensors (IF 3.4). Welcome to submit a paper.

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