Digital Healthcare Engineering

AI-Powered Medical Imaging

We harness the power of artificial intelligence to enhance the accuracy, speed, and interpretability of medical imaging. Our research focuses on developing cutting-edge deep learning methods for image reconstruction, denoising, segmentation, and disease detection across X-ray, CT, PET, and MRI. We explore innovative techniques like generative models, and self-supervised learning to push the boundaries of image-based data processing and diagnostics.

Biomedical Signal Acquisition & Processing

We develop robust systems for acquiring and interpreting physiological signals such as ECG, EEG, EMG, and PPG. By combining advanced sensing hardware with AI-driven signal processing algorithms, we aim to extract meaningful patterns for health monitoring, early diagnosis, and personalized therapy. Our work also involves wearable technologies and edge-AI to enable real-time, continuous monitoring in daily life

Medical Big Data & Foundation Models

We investigate how large-scale clinical data - including electronic health records, genomics, imaging, and wearable data - can be utilized to build predictive models for healthcare outcomes. We also focus on medical foundation models trained on diverse datasets to generalize across tasks such as diagnosis, treatment recommendation, and patient stratification. Our goal is to unlock the potential of big data for precision medicine.

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