Article

Deep learning algorithms for automatic segmentation of acute cerebral infarcts on diffusion-weighted images: Effects of training data sample size, transfer learning, and data features

2023-07-06

Abstract excerpt

<h4>Background</h4> Deep learning-based artificial intelligence techniques have been developed for automatic segmentation of diffusion-weighted magnetic resonance imaging (DWI) lesions, but currently mostly using single-site training data with modest sample sizes. <h4>Objective</h4> To explore the effects of 1) various sample sizes of multi-site vs. single-site training data, 2) domain adaptation, the utilization...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: e40aac48-d9a9-5cbd-a097-2ab0e71b30f2
DOI: 10.1101/2023.07.02.23292150

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Deep learning algorithms for automatic segmentation of acute cerebral infarcts on diffusion-weighted images: Effects of training data sample size, transfer learning, and data featuresDOI 10.1101/2023.07.02.23292150Read

Inter-Vendor Performance of Deep Learning in Segmenting Acute Ischemic Lesions on Diffusion-Weighted Imaging: A Multicenter StudySemantic proximity Read

Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic strokeSemantic proximity Read

Clinically Scalable Deep Learning for Stroke Multicenter Validation of an Ultra-Efficient NCCT-Based Diagnostic FrameworkSemantic proximity Read

Scaling behaviors of deep learning and linear algorithms for the prediction of stroke severitySemantic proximity Read

Machine Learning in Stroke Lesion Segmentation and Recovery Forecasting: A ReviewSemantic proximity Read

MAESTRO: A Public, Generalizable Model for Stroke Lesion Segmentation from T1 MRI Across the Recovery ContinuumSemantic proximity Read

External Validation of DeepBleed: The first open-source 3D Deep Learning Network for the Segmentation of Intracerebral and Intraventricular HemorrhageSemantic proximity Read

Generating Synthetic MR Perfusion Maps from DWI and FLAIR in Acute Ischemic Stroke: Development and External Validation of a Deep Learning ModelSemantic proximity Read

Deep Learning Approaches to Identify Patients within the Thrombolytic Treatment WindowSemantic proximity Read

Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data.Semantic proximity Read

AI-Based Pipeline for the Segmentation of White Matter Hypoattenuations in CT Scans: A Design-Choice Validation StudySemantic proximity Read

Automated Prediction of Ischemic Brain Tissue Fate from Multi-Phase CT-Angiography in Patients with Acute Ischemic Stroke Using Machine LearningSemantic proximity Read