Research
Research Interest
My main research interest lies in the interdisciplinary area of artificial intelligence (AI) and healthcare with a particular focus on medical image and signal analysis, e.g., medical image segmentation, focusing on building and verifying robust, data-efficient, reliable machine learning algorithms to scale up AI-powered medical image and signal analysis in real-world applications. Below are some topics I have explored:
- Multi-modal learning for healthcare, incl. image, signal, and texts (medical report) using large language models
- Robust machine learning
- Explainable machine learning
- Advanced data augmentation incl. adversarial data augmentation and generative models
- Data-efficient learning: self-supervised learning, few-shot learning, semi-supervised learning
- Multi-task learning
- Adaptive machine learning, including unsupervised domain adaptation at training/test time
- Algorithms with fairness, privacy, robustness, and interpretability in mind, including uncertainty-aware training, test time model calibration
I am extremely interested in their applications including:
- cardiac image analysis (segmentation, registration, and shape remodeling) with quality control (e.g. topology preserving, uncertainty measurement), follow-up motion/shape analysis, survival prediction, treatment planning
- Other medical image analysis inlc. prostate image analysis together with pathological image analysis, risk prediction, treatment planning; brain image segmentation and surface reconstruction for clinical applications.
- Signal processing and analysis, incl. ECG signal