Author

P. Raharja-Liu

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

A. Geraldes· 2 shared C. Bass· 2 shared D. Mehrotra· 2 shared J. Hense· 2 shared J. Schmidt· 2 shared P. Pandya· 2 shared S. Arslan· 2 shared S. Singhal· 2 shared

Activity by Year

2022-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersCancer Biology

A. Geraldes

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2 papers

Recent·Most cited

Deep learning can predict multi-omic biomarkers from routine pathology images: A systematic large-scale study

S. Arslan, D. Mehrotra, J. Schmidt, A. Geraldes, S. Singhal, J. Hense, X. Li, C. Bass, J. N. Kather, P. Pandya, P. Raharja-Liu

This study shows deep learning can predict multi-omic biomarkers from H&E-stained cancer slides, with 50% of models achieving AUC > 0.633 across 32 cancer types.

bioRxiv · Jan 21, 2022 · PreprintCancer Biology

Evaluation of a predictive method for the H&E-based molecular profiling of breast cancer with deep learning

S. Arslan, X. Li, J. Schmidt, J. Hense, A. Geraldes, C. Bass, K. Brown, A. Marcia, T. Dewhirst, P. Pandya, S. Singhal, D. Mehrotra, P. Raharja-Liu

The paper validates a deep learning method that predicts breast cancer biomarkers from H&E slides with high accuracy in a clinical setting.

bioRxiv · Jan 4, 2022 · PreprintCancer Biology