Bioimage Informatics Group (ruusuvuorilab)
University of Turku · Institute of Biomedicine
Our research focuses on data-intensive questions in cancer research and computational pathology. We use AI and machine learning to build tools capable of human-level accuracy for cancer diagnostics, grading and subtyping.
Recent News
September 2026
Talk at the European Society of Pathology Congress
Pekka gave a talk on virtual staining as an alternative to H&E at the European Society of Pathology Congress in Stockholm.
August 2026
2nd place at the BioCity Symposium
Nisar’s poster placed 2nd out of 108 entries at the 35th BioCity Symposium in Turku.
June 2026
Best Paper Award at ICTS4eHealth 2026
Our paper on GAN based virtual staining, led by Hyder Abbas, won the Best Paper Award at ICTS4eHealth 2026 in Portugal.
Latest Publications
Petäinen, L., Väyrynen, J.P., Böhm, J., Ruusuvuori, P., … Äyrämö, S. dMMR prediction from colorectal cancer histopathology: Leveraging non-tumor and low-magnification regions. Computer Methods and Programs in Biomedicine, 280, 109317, 2026.
Liimatainen, K., Latonen, L., Ruusuvuori, P. SparStVR — exploring sparse 3D histology data in virtual reality. Communications Engineering, 2026.
Khan, U., Härkönen, J., Friman, M., Hakimnejad, H., Latonen, L., Kuopio, T., … Ruusuvuori, P. Staining normalization in histopathology: Method benchmarking using multicenter dataset. Scientific Reports, 2026.
Tiihonen, A., Salonen, I., Koivisto, I., Ritamäki, A., Jaatinen, S., Hyvärinen, T., … Hypoxia shapes tumor immune microenvironment through cell-type dependent responses in diffuse astrocytomas. Cancer Research, 86(7_Supplement), 778–778, 2026.
Current Openings
No open positions listed at the moment. Motivated students interested in thesis projects (MSc or BSc) in computational pathology, bioimage informatics, or machine learning are welcome to get in touch.
For informal inquiries, please contact pekka.ruusuvuori(/at/)utu.fi
Research Focus
Digital pathology is rapidly transforming the workflow in routine diagnostics, and our goal is to enable faster, less subjective and in some cases even more accurate diagnostics through computational pathology enabled by modern machine learning. We anticipate that besides enabling decision support for tasks currently done by human experts, computational pathology has the potential for novel discoveries from histopathology beyond the limits of human vision.
Contact
Pekka Ruusuvuori, DSc (Tech), Associate Professor
pekka.ruusuvuori(/at/)utu.fi
📍 Institute of Biomedicine, MedD5, Kiinamyllynkatu 10, 20520 Turku, Finland