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Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients

https://doi.org/10.1007/s00330-021-08352-4
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27/27 checkable references clean · checked 2026-08-09

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

2 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 27 checked references that resolve
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An Updated Definition of Stroke for the 21st Century
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2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association
resolves10.1016/S0140-6736(14)60584-5
Effect of treatment delay, age, and stroke severity on the effects of intravenous thrombolysis with alteplase for acute ischaemic stroke: a meta-analysis of individual patient data from randomised trials
resolves10.1186/s41747-018-0061-6
Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine
resolves10.1038/s41591-018-0300-7
High-performance medicine: the convergence of human and artificial intelligence
resolves10.1007/s00234-018-2098-x
Automated ASPECT rating: comparison between the Frontier ASPECT Score software and the Brainomix software
resolves10.5853/jos.2016.00864
Epidemiology, Risk Factors, and Clinical Features of Intracerebral Hemorrhage: An Update
resolves10.1038/s41591-018-0147-y
Automated deep-neural-network surveillance of cranial images for acute neurologic events
resolves10.1038/s41746-017-0015-z
Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration
resolves10.1148/rg.246045065
Artifacts in CT: Recognition and Avoidance
resolves10.3174/ajnr.A6883
Artificial Intelligence and Acute Stroke Imaging
resolves10.1161/STROKEAHA.119.026068
Classification of Covert Brain Infarct Subtype and Risk of Death and Vascular Events
resolves10.1089/neu.2008.0590
Computer-Aided Assessment of Head Computed Tomography (CT) Studies in Patients with Suspected Traumatic Brain Injury
resolves10.1148/radiol.2017162664
Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging
resolves10.1117/12.2293725
Deep 3D convolution neural network for CT brain hemorrhage classification
resolves10.1016/S0140-6736(18)31645-3
Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
resolves10.1007/s00330-019-06163-2
Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network
resolves10.3174/ajnr.A5742
Hybrid 3D/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT
resolves10.1038/s41551-018-0324-9
An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets
resolves10.1007/s10278-018-00172-1
Improving Sensitivity on Identification and Delineation of Intracranial Hemorrhage Lesion Using Cascaded Deep Learning Models
resolves10.1007/s11760-012-0298-0
Intracranial hemorrhage detection using spatial fuzzy c-mean and region-based active contour on brain CT imaging
resolves10.1016/j.nicl.2017.02.007
PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT
resolves10.1109/ACCESS.2019.2906605
Segmenting Hemorrhagic and Ischemic Infarct Simultaneously From Follow-Up Non-Contrast CT Images in Patients With Acute Ischemic Stroke
resolves10.3390/data5010014
Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model
resolves10.1007/s10140-016-1440-z
Reformatted images improve the detection rate of acute traumatic subdural hematomas on brain CT compared with axial images alone
resolves10.1159/000500076
Collateral Automation for Triage in Stroke: Evaluating Automated Scoring of Collaterals in Acute Stroke on Computed Tomography Scans
The 2 references without a DOI — listed, not checked
no DOI — not checkedRonneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Springer International Publishing, Cham, pp 234–241
no DOI — not checkedBarreira CM, Rahman HA, Bouslama M et al (2018) Advance study: automated detection and volumetric assessment of intracerebral hemorrhage. European Stroke Journal European Stroke Organisation Conference: Abstracts 3:3–204
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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