Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods

Preprints.org(2022)10.20944/preprints202206.0112.v1Source: DataRank Database

A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods is a dataset published in Preprints.org (2022). On theSindex it has a DataRank of 0.460, placing it in the top 41.2% of the data-sharing corpus. It has been cited 8 times, with 5 citing works in its 1-hop citation network.

Top 41%percentile

0.460DataRank

Ranks in the top 41% for downstream scientific impact

Data paper Open Access8 citations · 5 citing works in its network

Free full text

Cite:

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)OA: greenTopics (28)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (1/3)

Dataset classification

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

DataRank Breakdown

Base Score 72%Citation Network 28%

Base Score Contribution

0.330

From this paper's citation signal

Citation Network Contribution

0.131

From 4 citing papers with measurable signal

Learn more about DataRank methodology →

Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 5 citers.

How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications
Bioengineering2023518 citations117 referencesContributes 0.053
A Survey on Physiological Signal-Based Emotion Recognition
Bioengineering202287 citations120 referencesContributes 0.037
Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network
Bioengineering20237 citations58 referencesContributes 0.036
An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies
Current Oncology202422 citations116 referencesContributes 0.027
A Review of Data Engineering in United States Healthcare Infrastructure
Healthcare20260 citations77 referencesContributes 0.000

Authors (7)

Sai Kiran Maryada ORCID,Warid Islam ORCID,Rowzat Faiz,Meredith Jones ORCID,Yuchen Qiu ORCID

Live enrichment

PubMed:30945415 OpenAlex:https://openalex.org/W4293077382

National Institutes of Health

Grant: 1P20GM135009-01A1

Oklahoma Center of Medical Imaging for Translational Cancer Research

National Institutes of Health

Grant: 3P30CA225520-06S4

Stephenson Cancer Center - Cancer Center Support Grant

greenLicense: cc-by

repositoryhttps://www.preprints.org/manuscript/202206.0112/v1/download

repositoryhttps://doi.org/10.20944/preprints202206.0112.v1

https://doi.org/10.3390/bioengineering9060256

https://pubmed.ncbi.nlm.nih.gov/35735499

Fields of Study

Radiomics and Machine Learning in Medical ImagingAI in cancer detection03 medical and health sciences0302 clinical medicine

Keywords

CADArtificial intelligenceComputer sciencePattern recognition (psychology)Computer-aided diagnosisTransfer of learningMachine learningFeature (linguistics)Support vector machineDeep learningRadiomicsClassifier (UML)AlgorithmEngineeringTechnologyQH301-705.5assessment of CAD performanceTbreast lesion classificationcomputer-aided diagnosis (CAD) schemesArticledeep transfer learningComputer Science and MathematicsBiology (General)

Sustainable Development Goals

SDG 0: Industry, innovation and infrastructure