Author

S. Punwani

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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Bioengineering· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Assay:imaging· 2 Approach:contrastive learning· 1 Research Focus:dimensionality reduction· 1 Organism:human· 1 Research Focus:imputation· 1

Frequent Co-Authors

E. Panagiotaki· 2 shared A. Ianus· 1 shared C. A. M. Gandini Wheeler-kingshott· 1 shared D. Atkinson· 1 shared D. C. Alexander· 1 shared E. Chiou· 1 shared F. Giganti· 1 shared F. Grussu· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersBioengineering

E. Panagiotaki

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Author profile →

2 papers

Recent·Most cited

Synthesizing VERDICT maps from standard DWI data using GANs

E. Chiou, V. Valindria, F. Giganti, S. Punwani, I. Kokkinos, E. Panagiotaki

This study synthesizes VERDICT maps from standard DWI data using GANs, showing strong correlation between synthetic and real maps for tumor area quantification.

bioRxiv · Feb 16, 2021 · PreprintBioengineering

"Select and retrieve via direct upsampling" network (SARDU-Net): a data-driven, model-free, deep learning approach for quantitative MRI protocol design

F. Grussu, S. B. Blumberg, M. Battiston, L. S. Kakkar, H. Lin, A. Ianus, T. Schneider, S. Singh, R. Bourne, S. Punwani, D. Atkinson, C. A. M. Gandini Wheeler-kingshott, E. Panagiotaki, T. Mertzanidou, D. C. Alexander

SARDU-Net is a deep learning framework for designing quantitative MRI protocols by selecting informative measurements from pilot scans, achieving high information content and reproducibility.

bioRxiv · May 26, 2020 · PreprintBioengineering