Research
01
Image Enhancement
We develop deep learning-based approaches to recover high-quality images from noisy measurements or degraded images.
Our goal is to enhance image quality while preserving the underlying textures and structures by adapting recent computer vision techniques to the specific tasks. Building on this work, we aim to extend our research to enable more reliable image interpretation and provide feasible solutions to real-world problems.
Research Tasks
- Denoising and artifact correction
- Super resolution
- Generative models
- Unsupervised and self-supervised learning
Selected Papers
02
Trustworthy AI
We conduct fundamental research to determine how reliable AI model is in bridging the gap between its prediction and ground truth. We assess model confidence based on uncertainty estimation and control the use of learned information and the measurement knowledge. Our goal is to establish AI model that can maintain meaningful information without hallucination.
Research Tasks
- Uncertainty quantification
- Model calibration
- Hallucination assessment
03
Dentistry AI
We develop AI methods for understanding dental images and connecting visual findings with clinical information. Our research spans lesion detection, anatomically guided lesion synthesis, data augmentation, and report-guided annotation using panoramic radiographs and cone-beam CT. Building on this work, we are exploring multimodal models and 3D anatomical reconstruction to support dental image interpretation.
Research Tasks
- Dental lesion detection
- Dental panoramic diagnosis
- Image-grounded report generation
- Vision-language understanding
- 3D dental reconstruction and anatomical modeling
04
Physics-based Imaging System
We design highly versatile new CT imaging systems by jointly implementing complex acquisition geometry, calibration, and image reconstruction. We are studying efficient reconstruction algorithms that can increase scanning coverage, even within the constraints of limited spaces. Our goal is to develop fast, memory-efficient methods for prototype imaging system applicable to real industrial CT.
Research Tasks
- 2D/3D image reconstruction
- Geometry calibration
- Multi-source CT system
- Field of view extension
Selected Papers
CT Meeting 2026, Oral Presentation
Multisource C-arm CT: Initial Results with a 2 x 2 Carbon Nanotube Source Array
05
Image Quality Evaluation
We investigate more clinically meaningful evaluation metrics in real practice to overcome the methodological representability limitations of widely used metrics. We pursue to design an image quality assessment capable of effectively evaluating a specific task, and to conduct a reader study from radiologists to further clinical feasibility.
Research Tasks
- No-reference image quality assessement
- MTF and NPS estimation
- Human perception evaluation
- Model observer
- Task-based image quality assessment
Selected Papers
Medical Physics, 2025 · Editor's Choice