Author

F. Sallo

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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Assay:imaging· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:large language model· 1

Frequent Co-Authors

A. Hagag· 2 shared A. Montesel· 2 shared H. Ahmed· 2 shared H. Faatz· 2 shared M. Arikan· 2 shared M. Book· 2 shared S. Ongun· 2 shared W. Lilaonitkul· 2 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 44 mutual co-author links

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2 shared papersBioengineering

A. Hagag

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2 papers

Recent·Most cited

OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers

M. Arikan, J. Willoughby, S. Ongun, F. Sallo, A. Montesel, H. Ahmed, A. Hagag, M. Book, H. Faatz, M. V. Cicinelli, A. A. Fawzi, D. Podkowinski, M. Cilkova, D. de Almeida, M. Zouache, , A. M. Dubis

The paper introduces OCT5k, a multi-disease and multi-graded annotated dataset for retinal layers to improve automated analysis of OCT scans.

bioRxiv · Mar 29, 2023Bioengineering

Deep Active Learning for Robust Biomedical Segmentation

M. Arikan, F. Sallo, A. Montesel, H. Ahmed, A. Hagag, M. Book, H. Faatz, M. Cicinelli, S. Meshkinfamfard, S. Ongun, A. Dubis, W. Lilaonitkul

This paper introduces a deep active learning framework that reduces annotation needs by 39-54% while improving robustness in medical image segmentation.

bioRxiv · Mar 28, 2023 · PreprintBioengineering