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Deeply-supervised density regression for automatic cell counting in microscopy images

Medical Image Analysis(2020)10.1016/j.media.2020.101892Source: DataRank Database

Deeply-supervised density regression for automatic cell counting in microscopy images is a research paper published in Medical Image Analysis (2020). On theSindex it has a DataRank of 0. It has been cited 80 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access80 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (10 grants)Impact (FWCI 8.04)OA: greenTopics (43)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Kyaw Thu Minn ORCID,Lilianna Solnica‐Krezel ORCID,Mark A. Anastasio ORCID,Hua Li ORCID,Shenghua He ORCID

Live enrichment

PubMed:33285481 OpenAlex:https://openalex.org/W3103117538

National Institutes of Health

Grant: 7R01EB020604-04

An Enabling Technology for Preclinical X-Ray Imaging of Biomaterials In-Vivo

National Institutes of Health

Grant: 5R01CA233873-03

Multimodal Biomarkers For Oropharyngeal Cancer

National Institutes of Health

Grant: 1R01EB023045-01A1

DEVELOPMENT OF A RAPID METHOD FOR IMAGING REGIONAL VENTILATION IN SMALL ANIMALS W/O CONTRAST AGENTS

National Institutes of Health

Grant: 1R21CA223799-01

RADIOMICS-BASED PROGNOSTIC MODEL OF CERVICAL CANCER HABITATS

National Institutes of Health

Grant: 5R01NS102213-03

Safe, rapid & functional pediatric brain imaging using photoacoustic computed tomography

NCI NIH HHS

Grant: R01 CA233873

NIBIB NIH HHS

Grant: R01 EB023045

NINDS NIH HHS

Grant: R01 NS102213

NCI NIH HHS

Grant: R21 CA223799

NIBIB NIH HHS

Grant: R01 EB020604

FWCI

8.04

Citation Percentile

1.0%

Citation Trend

2021

2022

2023

2024

2025

2026

greenLicense: Elsevier TDM

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/7856299

publisherhttps://api.elsevier.com/content/article/PII:S1361841520302565?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S1361841520302565?httpAccept=text/plain

journalhttps://doi.org/10.1016/j.media.2020.101892

Fields of Study

Cell Image Analysis TechniquesDigital Imaging for Blood DiseasesAI in cancer detection02 engineering and technology03 medical and health sciences0302 clinical medicine0202 electrical engineering, electronic engineering, information engineeringImage Processing, Computer-AssistedMicroscopyNeural Networks, Computer

MeSH Terms

Image Processing, Computer-AssistedMicroscopyNeural Networks, Computer

Keywords

Artificial intelligenceConvolutional neural networkComputer scienceRegressionPattern recognition (psychology)Variance (accounting)Image (mathematics)MicroscopyCell countingLinear regressionRegression analysisComputer visionMachine learningMathematicsStatisticsPathologyFOS: Computer and information sciencesComputer Science - Machine LearningComputer Vision and Pattern Recognition (cs.CV)Image and Video Processing (eess.IV)Computer Science - Computer Vision and Pattern RecognitionElectrical Engineering and Systems Science - Image and Video ProcessingMachine Learning (cs.LG)Image Processing, Computer-AssistedFOS: Electrical engineering, electronic engineering, information engineeringNeural Networks, ComputerAutomatic cell countingDeeply-supervised learningFully convolutional neural networkMicroscopy images