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AI in the Loop: functionalizing fold performance disagreement to monitor automated medical image segmentation workflows

Frontiers in Radiology(2023)10.3389/fradi.2023.1223294Source: DataRank Database

AI in the Loop: functionalizing fold performance disagreement to monitor automated medical image segmentation workflows is a research paper published in Frontiers in Radiology (2023). On theSindex it has a DataRank of 0. It has been cited 1 time.

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Open Access1 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (5 grants)OA: goldTopics (12)IDs (PubMed, PMC)Annotations (1)

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Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (4)

Panagiotis Korfiatis ORCID,Adriana Gregory ORCID,Timothy L. Kline ORCID,Harrison C. Gottlich ORCID

Live enrichment

PubMed:37780641 PMC:PMC10540615

Kidney Diseases of the National Institutes of Health

Grant: K01DK110136, R03DK125632

NIDDK NIH HHS

Grant: K01 DK110136

NIDDK NIH HHS

Grant: R03 DK125632

National Institutes of Health

Grant: 5K01DK110136-05

Advanced MR Imaging and Image Analytics as a Precision Medicine Tool to Manage ADPKD

National Institutes of Health

Grant: 5R03DK125632-02

Artificial Intelligence-Based Approaches for Renal Structure Characterization in Computed Tomography Images

goldLicense: CC BY

publisherhttps://www.frontiersin.org/articles/10.3389/fradi.2023.1223294/full

Europe_PMChttps://europepmc.org/articles/PMC10540615

Europe_PMChttps://europepmc.org/articles/PMC10540615?pdf=render

https://doi.org/10.3389/fradi.2023.1223294

https://pubmed.ncbi.nlm.nih.gov/37780641

Fields of Study

0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Epistemic UncertaintyDeep LearningSimilarity MetricsConvolutional Neural NetworksSemantic SegmentationAi In The LoopMachine Learning Model PerformanceMedical physics. Medical radiology. Nuclear medicineR895-920Radiology