Author

H. S. Muti

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· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:imaging· 2 Assay:clinical cohort / trial· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

C. Trautwein· 3 shared J. N. Kather· 3 shared A. T. Pearson· 2 shared C. M. L. Loeffler· 2 shared N. Ghaffari Laleh· 2 shared T. Luedde· 2 shared A. Echle· 1 shared A. Srisuwananukorn· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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3 shared papersCancer Biology

C. Trautwein

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3 papers

Recent·Most cited

DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology

M. van Treeck, D. Cifci, N. Ghaffari Laleh, O. L. Saldanha, C. M. L. Loeffler, K. J. Hewitt, H. S. Muti, A. Echle, T. Seibel, T. P. Seraphin, C. Trautwein, S. Foersch, T. Luedde, D. Truhn, J. N. Kather

DeepMed provides a modular, scalable AI pipeline for predicting molecular and histopathological features from histopathology images, with public benchmark data.

bioRxiv · Dec 19, 2021 · PreprintCancer Biology

Classical Mathematical Models for Prediction of Response to Chemotherapy and Immunotherapy

N. Ghaffari Laleh, C. M. L. Loeffler, J. Grajek, K. Stankova, A. T. Pearson, H. S. Muti, C. Trautwein, H. Enderling, J. Poleszczuk, J. N. Kather

This study evaluates classical mathematical models for predicting chemotherapy and immunotherapy responses using real-world patient tumor volume data, finding the Gompertz model provides the best balance between fit and complexity.

bioRxiv · Oct 23, 2021 · PreprintCancer Biology

Deep learning detects virus presence in cancer histology

J. N. Kather, J. Schulte, H. I. Grabsch, C. Loeffler, H. S. Muti, J. Dolezal, A. Srisuwananukorn, N. Agrawal, S. Kochanny, S. von Stillfried, P. Boor, T. Yoshikawa, D. Jaeger, C. Trautwein, P. Bankhead, , A. T. Pearson

This paper uses deep learning to detect the presence of HPV and EBV in cancer histology images, achieving high accuracy in predicting virus-associated tumors.

bioRxiv · Jul 5, 2019 · PreprintCancer Biology