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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.

Contact

Mattias Rantalainen, PhD

mattias.rantalainen@ki.se

https://ki.se/en/people/matran

 

Department of Medical Epidemiology and Biostatistics (MEB)

Karolinska Institute

Stockholm, Sweden



Supported by

The Swedish Cancer Foundation

The Swedish Research Council

Karolinska Institutet

 

Preprints


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.

https://openreview.net/forum?id=W9sz0zHk33h


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

arXiv:2106.14256