Welcome to Smart Lab!
Smart Lab, led by Prof. Hao Chen, is committed to pushing the boundaries of trustworthy AI technologies for healthcare and science. Our research directions include large-scale models for healthcare, computer-assisted intervention, AI for science, and bioinformatics, etc. Our ultimate goal is to spearhead a transformative revolution in medical practices and scientific discoveries, paving the way for a brighter and healthier future.
Recent News
[IEEE RBME] Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions
HKUST Smart Lab leads a comprehensive survey on Healthcare Foundation Models (HFM) in IEEE Reviews in Biomedical Engineering (IEEE RBME, IF=17.2), exploring the current developments, challenges, and future directions of HFMs in healthcare.
Last updated on 2024/11/13
[Press Release] HKUST Smart Lab Spearheads Four Large AI Models to Revolutionize Healthcare
Smart Lab at HKUST has unveiled four AI-driven models poised to transform the medical and healthcare fields, assisting both general and specialist medical practitioners in diagnosing and prognosing up to 30 types of cancers and diseases.
Last updated on 2024/11/01
Towards Large-Scale 3D Medical Image Pre-training with Geometric Context Priors
We introduce the 3D Medical Vision Foundation Model VoCo. The proposed method aims to leverage geometric context priors for self-supervision. This paper also introduces a new benchmark and investigates the scaling laws of medical foundation models.
Last updated on 2024/10/16
UG Students at HKUST SMART Lab Won Tse Cheuk Ng Tai Scholarship 2024
HKUST CSE UG student Mr. Runsheng LIU was selected as 2023-2024's Tse Cheuk Ng Tai Scholarship recipient with his UROP study titled 'Deep learning for medical image analysis'. Congratulations!
Last updated on 2024/09/27
[IEEE-TNNLS] Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation
A new study on Optical Coherence Tomography Angiography (OCTA) image super-resolution from the HKUST Smart Lab has been accepted by the IEEE Transactions on Neural Networks and Learning Systems.
Last updated on 2024/09/19
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