Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Fisheye transformation enhances deep-learning-based single-cell phenotyping by including cellular microenvironment.

Cell reports methods - 19 Dec 2022

Toth Timea, Bauer David, Sukosd Farkas, Horvath Peter

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cell Culture Techniques
Share to Cellular Microenvironment

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to Microscopy

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 36590690
DOI: 10.1016/j.crmeth.2022.100339
PMCID: PMC9795324

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Fisheye transformation enhances deep-learning-based single-cell phenotyping by including cellular microenvironment.PMID 36590690Read ↗

Image-based cell phenotyping with deep learning.Semantic proximity Read ↗

Microenvironmental information significantly improves the recognition of cell types in human lung cancer patientsSemantic proximity Read ↗

Image-based Cell Phenotyping Using Deep LearningSemantic proximity Read ↗

Environmental properties of cells improve machine learning-based phenotype recognition accuracy.Semantic proximity Read ↗

Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.Semantic proximity Read ↗

Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).Semantic proximity Read ↗

FocA: A deep learning tool for reliable, near-real-time imaging focus analysis in automated cell assay pipelinesSemantic proximity Read ↗

Phenotypic Characterization Using Open-Source Deep Learning Tools.Semantic proximity Read ↗

SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biologySemantic proximity Read ↗

Using Dimensionality Reduction to Visualize Phenotypic Changes in High-Throughput Microscopy.Semantic proximity Read ↗

Image-based phenotyping of disaggregated cells using deep learning.Semantic proximity Read ↗

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.