Mattias Rantalainen

Senior lecturer

The Predictive Medicine group focus on medical research that is driven by statistical machine learning, artificial intelligence and big data. Currently our main focus is on computational pathology.

About me

I am an associate professor (docent) at the Department of Medical Epidemiology and Biostatistics where I lead the Predictive Medicine group. 

I was previously a research fellow (2009-2013) with a joint affiliation at the Department of Statistics (University of Oxford) and at the Wellcome Trust Centre for Human Genetics (University of Oxford) where I worked with Professor Chris Holmes and Dr. Cecilia Lindgren. I was awarded a MRC Centenary Early Career Award (2012-2013) and a Medical Research Council (MRC) Special Training Fellowship in Biomedical Informatics (2009-2012). I had a postdoctoral position working on the data analysis work package for the MolPAGE consortium in Prof Chris Holmes’ group at the Department of Statistics in Oxford (2008).  I completed my PhD at Imperial College London where I developed novel multivariate pattern recognition methods with applications in metabonomics, working together with Professor Elaine Holmes and Professor Jeremy Nicholson.  I have an undergraduate degree in Engineering Biology (combined BSc/MSc) from Umeå University in Sweden.

Research description

The Predictive Medicine group focus on medical research that is driven by statistical machine learning, artificial intelligence (AI) and large population representative data sets. Currently our research is centered on projects in the computational pathology domain.

We develop and apply statistical and machine learning methodologies for predictive modelling in biomedical applications with a particular interest in precision medicine and cancer research.

Our mission is to drive the development and application of data-driven approaches in cancer precision medicine and to develop and validate novel patient stratification models, including prognostic and treatment predictive applications. To achieve this we develop methods and models that allow us to transform large biomedical data into clinically relevant predictions at the individual level. Some of our research is focused on clinical translation and implementation.

Our research is based on large datasets (big-data) across multiple modalities including comprehensive molecular profiling (e.g. DNA- and RNA-sequencing), clinical information and medical imaging data (histopathology).

Main research areas

  • Computational pathology: AI (deep learning) driven research focusing on developing and validating models in the domain of cancer histopathology. The research is based on large population representative studies (we have >70,000 WSIs collected in-house). The main focus is on breast, prostate and colorectal cancer ( & I coordinate the Swedish AI Precision Pathology (SwAIPP) consortium, focusing on translation and implementation of AI-based pathology (
  • Cancer precision medicine: Development and validation of predictive models for improved patient stratification, based on comprehensive molecular phenotyping data (e.g. DNA- and RNA-sequencing)

Current research grants (as PI)

Our work is supported by Swedish Research Council (VR), Swedish Cancer Society, ERA PerMed, MEDTECHLABS, VINNOVA, SWElife, SeRC and Karolinska Institutet.

Doctoral students

  • Philippe Weitz (main supervisor)
  • Abhinav Sharma (main supervisor)
  • Dusan Rasic (co-supervisor)
  • Sandra Kristiane Sinius Pouplier  (co-supervisor)
  • Tewodros Yalew (co-supervisor)
  • Quang Thinh Trac (co-supervisor)
  • Venkatesh Chellappa Patel (co-supervisor)
  • Xiaoyang Du (co-supervisor)

Other group members

  • Yanbo Feng (Postdoc)
  • Leslie Solorzano Vargas (Postdoc)
  • Bojing Liu (Postdoc)
  • Constance Boissin (Postdoc)
  • Duong Tran (Project assistant)
  • Yujie Xiang (Research assistant)
  • Ariane Buckenmeyer (Research technician)
  • Viktoria Sartor (MSc student)

Former group members / supervised students

  • Yinxi Wang (PhD student, main supervisor)
  • Peter Ström (PhD student, co-supervisor)
  • Pablo Gonzalez Ginestet (PhD student, co-supervisor)
  • Charlotte Von Heijne Widlund (visiting scientist)
  • Mei Wang (Postdoc; currently Research Assistant Professor, School of life science, Peking University, China)
  • Arvind Mer (Postdoc, currently Assistant Professor, University of Ottawa, Canada)
  • Nghia Vu (Postdoc; currently Assistant Professor, Karolinska Institutet, MEB)
  • Daniel Garcia León (Postdoc)
  • Astrid Helzen (Technician)
  • Balasz Acs (Associated postdoc)
  • Kajsa Ledesma Eriksson (MSc student)
  • Sandy Kang Lövgren (MSc student)
  • Anton Normelius (MSc student)
  • Boxi Zhang (MSc student)
  • Youcheng Zhang (MSc student)

Examples of publications

Wang Y, Acs B, Robertson S, Liu B, Solorzano L, Wählby C,  Hartman J, Rantalainen M. Improved breast cancer histological grading using deep learning, Annals of Oncology. 2022 33 (1), 89-98

Wang Y, Kartasalo K, Valkonen M, Larsson C, Ruusuvuori P, Hartman J, Rantalainen M. Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer. Cancer Research. 2021 81 (19), 5115-5126

Weitz P, Wang Y, Hartman J, Rantalainen M. An investigation of attention mechanisms in histopathology whole-slide-image analysis for regression objectives. In Proceedings of the IEEE/CVF International Conference on Computer Vision 2021 (pp. 611-619).

Acs B, Rantalainen M, Hartman J., Artificial intelligence as the next step towards precision pathology. Journal of Internal Medicine. 2020 Mar 3.

Liu B, Wang Y, Weitz P,  Lindberg J, Egevad L, Grönberg H, Eklund M, Rantalainen M. Using deep learning to detect patients at risk for prostate cancer despite benign biopsies. iScience. 2022 25 (7) 104663

Weitz P, Wang Y, Kartasalo K, Egevad L, Lindberg J, Grönberg H, Eklund M, Rantalainen M. Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks. Bioinformatics. 2022 38 (13), 3462-3469

Ström, P., Kartasalo, K., Olsson, H., Solorzano, L., Delahunt, B., Berney, D.M., Bostwick, D.G., Evans, A.J., Grignon, D.J., Humphrey, P.A., Iczkowski, K.A.. Kench, J.G., Kristiansen, G., van der Kwast, T.H., Leite, K.R.M., McKenneym, J.K., Oxley, J., Pan, C.C., Samaratunga, H., Srigley, J.R., Takahashi, H., Tsuzuki, T., Varma, M.,Zhou, M., Lindberg, J.,  Lindeskog,  C., Ruusuvuori, P, Wählby, C., Grönberg, H.,  Rantalainen,  M., Egevad, L., Eklund, M.,   Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. The Lancet Oncology. 2020 Jan 8.

Wang, M., Lindberg, J., Klevebring, D., Nilsson, C., Lehmann, S., Grönberg, H., Rantalainen, M., Development and validation of a novel RNA sequencing-based prognostic score for acute myeloid leukemia, J Natl Cancer Inst, 2018 Mar 18

Vu, T.N., Wills, Q.F., Kalari, K.R., Niu, N., Wang, L., Pawitan, Y., Rantalainen M., Isoform-level gene expression patterns in single-cell RNA-sequencing data, Bioinformatics, 2018 Feb 1

Wang, M., Klevebring, D., Lindberg, K., Czene, K., Grönberg, H., Rantalainen M.; Determining breast cancer histological grade from RNA sequencing data. Breast Cancer Research, 2016, 18(1).

Vu, N.T., Wills, Q.F., Kalari, K.R., Niu, N., Wang, L., Rantalainen M.§, Pawitan Y.§. Beta-Poisson model for single-cell RNA-seq data analyses. Bioinformatics, 2016, btw202.


Teaching portfolio