Computational Pathology
Whole-slide image analysis, tumor segmentation, tissue phenotyping, and quantitative pathology tools for more precise biomedical interpretation.
Research
Visual intelligence for biomedical problems that matter.
VIBE Lab develops AI methods for medical image analysis with a strong focus on practical biomedical use. We work on segmentation, representation learning, computational pathology, multimodal biomedical AI, and usable research software that can help models move from papers into real workflows.
Featured Project
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation (COSAS 2024)
A domain generalization challenge for adenocarcinoma segmentation across organ, scanner, staining, and acquisition shifts. COSAS connects our work in robust visual intelligence, computational pathology, and practical biomedical AI evaluation.
Research Directions
View publicationsBiomedical Image Analysis
Robust denoising, segmentation, microscopy analysis, and visual measurement methods designed around real data quality and acquisition constraints.
Multimodal Biomedical AI
Learning from images together with reports, clinical context, molecular signals, and metadata to make biomedical AI more grounded and useful.
Usable AI Software
We value aesthetics as part of research impact: software should be clear, reliable, easy to inspect, and pleasant enough that people actually use it.