Author

A. Echle

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· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Assay:imaging· 2 Approach:deep learning· 1 Approach:experimental assay· 1

Frequent Co-Authors

J. N. Kather· 2 shared S. Foersch· 2 shared A. Bychkov· 1 shared C. M. L. Loeffler· 1 shared C. N. Heinz· 1 shared C. Trautwein· 1 shared D. Cifci· 1 shared D. Truhn· 1 shared

Activity by Year

2021-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersCancer Biology

J. N. Kather

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Author profile →

2 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

The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups

C. N. Heinz, A. Echle, S. Foersch, A. Bychkov, J. N. Kather

The paper surveys computational pathology experts to identify promising AI applications in digital pathology, finding treatment response prediction and genetic alteration detection as top priorities.

bioRxiv · Dec 16, 2021Pathology