ArticleDiagnostics (Basel, Switzerland)2023
Sequence-Type Classification of Brain MRI for Acute Stroke Using a Self-Supervised Machine Learning Algorithm.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Brain Stroke Classification Using CT Scans with Transformer-Based Models and Explainable AI.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning.Insights into imaging · 2025Article
- Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.Sensors (Basel, Switzerland) · 2024Article
- Brain MRI sequence and view plane identification using deep learning.Frontiers in neuroinformatics · 2024Article
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Authors and funding
11 authors.
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Abstract
We propose a self-supervised machine learning (ML) algorithm for sequence-type classification of brain MRI using a supervisory signal from DICOM metadata (i.e., a rule-based virtual label). A total of 1787 brain MRI datasets were constructed, including 1531 from hospitals and 256 from multi-center trial datasets. The ground truth (GT) was generated by two experienced image analysts and checked by a radiologist. An ML framework called ImageSort-net was developed using various features related to MRI acquisition parameters and used for training virtual labels and ML algorithms derived from rule-based labeling systems that act as labels for supervised learning. For the performance evaluation of ImageSort-net (ML
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Registered trials
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