Author

T. Ushiku

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 Microbiology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

D. Komura· 2 shared H. Katoh· 2 shared S. Ishikawa· 2 shared A. Kawabe· 1 shared G. Furuya· 1 shared H. Iwase· 1 shared H. Koda· 1 shared H. Konishi· 1 shared

Activity by Year

2020-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersPathology

D. Komura

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Can SARS-CoV-2 transmit from a dead body?

K. Iwatsuki-Horimoto, H. Ueki, M. Ito, S. Nagasawa, Y. Hirata, K. Hashizume, K. Ushiwata, H. Iwase, Y. Makino, T. Ushiku, S. Akitomi, M. Imai, H. Saitoh, Y. Kawaoka

This study investigates SARS-CoV-2 transmission from dead bodies using a hamster model, finding that transmission is possible but can be prevented by postmortem care and embalming.

bioRxiv · Aug 29, 2022 · PreprintMicrobiology

Beyond pathologist-level annotation of large-scale cancer histology for semantic segmentation using immunofluorescence restaining

D. Komura, T. Onoyama, K. Shinbo, H. Odaka, M. Hayakawa, M. Ochi, R. Herdiantoputri, K. Sakamoto, H. Katoh, T. Ikeda, T. Ushiku, S. Ishikawa

The paper develops SegPath, a large annotation dataset for histopathology segmentation, showing it outperforms pathologist annotations and reduces bias.

bioRxiv · May 9, 2022Pathology

Deep Texture Representations as a Universal Encoder for Pan-cancer Histology

D. Komura, A. Kawabe, K. Fukuta, K. Sano, T. Umezaki, H. Koda, R. Suzuki, K. Tominaga, H. Konishi, S. Nishida, G. Furuya, H. Katoh, T. Ushiku, M. Fukayama, S. Ishikawa

This paper introduces deep texture representations (DTRs) as a universal encoder for cancer histology, enabling accurate prediction of genomic features and cancer types from histological images.

bioRxiv · Jul 28, 2020Cancer Biology