CHIME develops methodology and models for histopathology assessment with the aim to improve the precision in cancer diagnosis. We use large-scale studies and modern machine learning methodologies to explore the boundaries of image-based diagnostics.


CHIME currently includes studies in the areas of breast, prostate and colorectal cancer.


Mattias Rantalainen, PhD




Department of Medical Epidemiology and Biostatistics (MEB)

Karolinska Institute

Stockholm, Sweden

Supported by

The Swedish Cancer Foundation

The Swedish Research Council

Karolinska Institutet



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. arXiv preprint arXiv:2009.08917. 2020 Sep 18.

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. arXiv preprint arXiv:2104.09310. 2021 Apr 19.

Weitz P, Acs B, Hartman J, Rantalainen M. Prediction of Ki67 scores from H&E stained breast cancer sections using convolutional neural networks.


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. arXiv preprint