Author

A.-K. Thebille

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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Pathology· 1

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

C. F. Krebs· 1 shared C. Kuppe· 1 shared E. Hoxha· 1 shared F. Braun· 1 shared J. Kranz· 1 shared L. Gernhold· 1 shared M. Halder· 1 shared M. Klaus· 1 shared

Activity by Year

2020-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperPathology

C. F. Krebs

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1 paper

Recent·Most cited

Deep learning-based molecular morphometrics for kidney biopsies

M. Zimmermann, M. Klaus, M. N. Wong, A.-K. Thebille, L. Gernhold, C. Kuppe, M. Halder, J. Kranz, N. Wanner, F. Braun, S. Wulf, T. Wiech, U. Panzer, C. F. Krebs, E. Hoxha, , V. G. Puelles

The paper develops a deep learning method for analyzing kidney biopsy images to quantify podocyte loss, identifying morphometric signatures in ANCA-GN patients for risk stratification.

bioRxiv · Aug 23, 2020Pathology