Author

S. Hari

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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Biophysics· 1 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Research Focus:imputation· 1 Approach:machine learning (classical)· 1 Research Focus:software / tool· 1 Approach:statistical model· 1

Frequent Co-Authors

A. Houtsmuller· 1 shared C. S. Smith· 1 shared C. W. Hagen· 1 shared D. Brundage· 1 shared E. M. Van Allen· 1 shared H. Ella· 1 shared J. Nyman· 1 shared J. P. Hoogenboom· 1 shared

Activity by Year

2021-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperBiophysics

A. Houtsmuller

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Author profile →

2 papers

Recent·Most cited

Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology

J. Rosenthal, R. Carelli, M. Omar, D. Brundage, H. Ella, J. Nyman, S. Hari, E. M. Van Allen, M. Luigi, R. Umeton, M. Loda

The paper introduces PathML, a computational pathology toolkit designed for scalability, standardization, and ease of use in cancer research, with applications demonstrated across diverse use-cases.

bioRxiv · Oct 21, 2021Cancer Biology

Structured illumination microscopy with noise-controlled image reconstructions

C. S. Smith, J. Slotman, L. Schermelleh, N. Chakrova, S. Hari, Y. Vos, C. W. Hagen, M. Mueller, W. van Cappellen, A. Houtsmuller, J. P. Hoogenboom, S. Stallinga

This paper introduces noise-controlled reconstruction methods for structured illumination microscopy to reduce artefacts and improve image quality.

bioRxiv · Mar 11, 2021Biophysics