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

[Nature Communications] A Large Model for Non-invasive and Personalized Management of Breast Cancer from Multiparametric MRI

HKUST's Smart Lab, in collaboration with leading institutions, has developed a groundbreaking mpMRI-based model for personalized breast cancer management, now published in Nature Communications.

Last updated on 2025/04/21

[Nature Biomedical Engineering] Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Nature Sub-Journal Breakthrough! HKUST Smart Lab Team Pioneers First Generalizable Pathology Foundation Model (GPFM), Solving the "Versatility" Challenge in AI Healthcare.

Last updated on 2025/04/16

[CVPR 2025] FOCUS: Knowledge-Enhanced Adaptive Visual Compression for Few-Shot Whole Slide Image Classification

HKUST Smart Lab presents FOCUS, a novel knowledge-enhanced adaptive visual compression framework for few-shot whole slide image classification, accepted at CVPR 2025!

Last updated on 2025/02/28

Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation

See our latest work, a new paradigm for cervical cancer screening that leverages large-scale pretraining and test-time adaptation to achieve robust generalization across diverse clinical settings. Our Smart-CCS framework aims to overcome longstanding challenges in cytology-based cancer screening.

Last updated on 2025/02/17

[MIA2025] MedlAnomaly: A Comparative Study of Anomaly Detection in Medical Images

A new survey from HKUST Smartlab presents a comprehensive benchmark for medical anomaly detection, evaluating 30 representative methods across seven diverse datasets.

Last updated on 2025/02/15

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We want you!

We are always looking for self-motivated and determined talents. Open positions are available at our Recruitment page.


Last Update: Apr 21, 2025