ArticleCell reports methods2022
Fisheye transformation enhances deep-learning-based single-cell phenotyping by including cellular microenvironment.
Article in Cell reports methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- AI-based digital pathology and spatial proteomics enable precision oncology: A case report of recurrent melanoma in a young patient.NPJ precision oncology · 2026Article
- A comprehensive update on the application of high-throughput fluorescence imaging for novel drug discovery.Expert opinion on drug discovery · 2025Review
- Collective properties of Petitella georgiae in tube environments.Scientific reports · 2024Article
- Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium.Molecular & cellular proteomics : MCP · 2024Article
- An introduction to representation learning for single-cell data analysis.Cell reports methods · 2023Review
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4 authors.
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Abstract
Incorporating information about the surroundings can have a significant impact on successfully determining the class of an object. This is of particular interest when determining the phenotypes of cells, for example, in the context of high-throughput screens. We hypothesized that an ideal approach would consider the fully featured view of the cell of interest, include its neighboring microenvironment, and give lesser weight to cells that are far from the cell of interest. To satisfy these criteria, we present an approach with a transformation similar to those characteristic of fisheye cameras. Using this transformation with proper settings, we could significantly increase the accuracy of single-cell phenotyping, both in the case of cell culture and tissue-based microscopy images, and we present improved results on a dataset containing images of wild animals.
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