Author

A. Bychkov

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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Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Assay:imaging· 3 Approach:deep learning· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

J. Fukuoka· 2 shared K. Tabata· 2 shared S. Morimoto· 2 shared Y. Kashima· 2 shared A. C. Roden· 1 shared A. Cavazza· 1 shared A. Echle· 1 shared A. M. Marchevsky· 1 shared

Activity by Year

2021-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersPathology

J. Fukuoka

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Deep learning for histopathological subtyping and grading of lung adenocarcinoma

K. Lami, N. Ota, S. Yamaoka, A. Bychkov, K. Matsumoto, W. Uegami, R. Attanoos, S. Berezowska, L. Brcic, A. Cavazza, J. English, A. T. Fabro, K. Ishida, Y. Kashima, Y. Kitamura, , J. Fukuoka

This study develops CNNs to subtype and grade lung adenocarcinoma, achieving high accuracy and outperforming most pathologists in grading prediction.

bioRxiv · Nov 2, 2022 · PreprintPathology

Collaborative workflow between pathologists and deep learning for evaluation of tumor cellularity in lung adenocarcinoma

T. Sakamoto, T. Furukawa, H. H. N. Pham, K. Kuroda, K. Tabata, Y. Kashima, E. N. Okoshi, S. Morimoto, A. Bychkov, J. Fukuoka

This paper develops an AI model for tumor cellularity evaluation in lung adenocarcinoma, validated through collaboration with pathologists to improve accuracy.

bioRxiv · Jan 11, 2022 · PreprintPathology

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