Research
My work focuses on computational methods for medical imaging and digital pathology, building reproducible pipelines that bridge classical image processing and AI-ready systems.
Research lines
Digital Pathology & WSI Registration
Development of registration pipelines for whole slide images (WSI), enabling alignment of serial histology sections for volumetric tissue reconstruction.
- Multi-resolution alignment strategies
- Rigid and nonrigid registration
- Integration with segmentation workflows
- Preparation for 3D rendering pipelines
Medical Image Computing
- Brain image registration and segmentation
- Skin lesion classification using ML/DL
- Lung MRI inhalation/exhalation registration
Focus on clinically meaningful validation and reproducibility.
Computer Vision
Instance segmentation for coronary artery disease angiography using classical image processing and machine learning approaches, compared with deep learning techniques.
Research vision
I aim to build computational infrastructures that allow scalable digital pathology analysis, where classical image processing ensures robustness and AI modules enhance feature extraction, segmentation, and predictive modeling.
The long-term objective is to contribute to clinically meaningful, AI-assisted diagnostic workflows.
Tutorials
Practical digital pathology tutorials (e.g., QuPath workflows), image analysis habits, and reproducible research tooling. New videos will appear automatically as I publish them.
Clinical training presentation
View-only presentation with clinical background and context.
For viewing purposes only.
Publications & posters
This section will include peer-reviewed publications, conference posters, and technical reports related to digital pathology and medical imaging.