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Deep learning-based Segmentation of Multi-site Disease in Ovarian Cancer

medRxiv(2023)10.1101/2023.01.10.22279679Source: DataRank Database

Deep learning-based Segmentation of Multi-site Disease in Ovarian Cancer is a research paper published in medRxiv (2023). On theSindex it has a DataRank of 0.312. It has been cited 7 times.

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0.312DataRank · unranked

Scored on demand from live citation data

Open Access7 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (7 grants)OA: greenTopics (41)IDs (PubMed, OpenAlex)SDGs (2)

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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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.312

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

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Authors (27)

Leonardo Rundo ORCID,Ramona Woitek ORCID,L. Escudero ORCID,Lucian Beer ORCID,Mireia Crispin-Ortuzar ORCID

Live enrichment

PubMed:19022398 OpenAlex:https://openalex.org/W4315618870

UK Research and Innovation

Grant: EP/S026045/1

PET++: Improving Localisation, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomised Optimisation

UK Research and Innovation

Grant: EP/N014588/1

EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging

UK Research and Innovation

Grant: EP/T017961/1

Cambridge Mathematics of Information in Healthcare (CMIH)

European Commission

Grant: 691070

CHallenges in Preservation of Structure

Wellcome Trust

Grant: unidentified

unidentified

UK Research and Innovation

Grant: EP/T003553/1

Robust and Efficient Analysis Approaches of Remote Imagery for Assessing Population and Forest Health in India

European Commission

Grant: 777826

Nonlocal Methods for Arbitrary Data Sources

greenLicense: cc-by

repositoryhttps://www.medrxiv.org/content/medrxiv/early/2023/01/11/2023.01.10.22279679.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.01.10.22279679

repositoryhttps://doi.org/10.1101/2023.01.10.22279679

repositoryhttps://www.repository.cam.ac.uk/handle/1810/346419

Fields of Study

Radiomics and Machine Learning in Medical ImagingOvarian cancer diagnosis and treatmentMRI in cancer diagnosis03 medical and health sciences0302 clinical medicine

Keywords

Ovarian cancerSegmentationMedicineSerous fluidRadiologyDeep learningArtificial intelligenceComputer scienceCancerInternal medicinedeep learning; omentum; ovarian Neoplasms; pelvis; tomography; x-ray computedR895-92032 Biomedical and Clinical SciencesBioengineeringHealth Disparities and Racial or Ethnic Minority Health ResearchTomography (x-ray computed)PelvisMedical physics. Medical radiology. Nuclear medicineRare DiseasesTechnical NoteMachine Learning and Artificial IntelligenceHumansMinority HealthOvarian Neoplasms3211 Oncology and CarcinogenesisDeep learning; Omentum; Ovarian Neoplasms; Pelvis; Tomography (x-ray computed)Health DisparitiesOvarian CystsNetworking and Information Technology R&D (NITRD)Networking and Information Technology R&D (NITRD)Women's HealthBiomedical ImagingFemaleNeural Networks, ComputerTomography, X-Ray ComputedOmentum

Sustainable Development Goals

SDG 3: 3. Good healthSDG 0: Good health and well-being