ArticleRadiology. Artificial intelligence2024
Examination-Level Supervision for Deep Learning-based Intracranial Hemorrhage Detection on Head CT Scans.
Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 13 citations in OpenAlex.
- Cross-Dataset Generalization of Deep Learning-Based Detectors for Intracranial Hemorrhage Subtype Localization on Noncontrast Head CT: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Robust slice-level stroke classification in non-contrast head CT via structural consistency regularization and counterfactual suppression.Frontiers in bioengineering and biotechnology · 2026Article
- Deep learning for intracranial hemorrhage detection and classification in brain CT scans: a systematic review and hybrid model approach.Frontiers in digital health · 2026Review
- The Evolution of Radiology Image Annotation in the Era of Large Language Models.Radiology. Artificial intelligence · 2025Review
- High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis.NPJ digital medicine · 2025Article
- Diagnostic Accuracy of Deep Learning for Intracranial Hemorrhage Detection in Non-Contrast Brain CT Scans: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset.Radiology. Artificial intelligence · 2024Article
- Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels.Radiology. Artificial intelligence · 2024Article
- Achieving More with Less: Combining Strong and Weak Labels for Intracranial Hemorrhage Detection.Radiology. Artificial intelligence · 2024Article
- Gradient-Based Saliency Maps Are Not Trustworthy Visual Explanations of Automated AI Musculoskeletal Diagnoses.Journal of imaging informatics in medicine · 2024Article
- Weak Supervision, Strong Results: Achieving High Performance in Intracranial Hemorrhage Detection with Fewer Annotation Labels.Radiology. Artificial intelligence · 2024Article
- Artificial intelligence for traumatic brain injury imaging: a translational review from algorithm development to clinical implementation.Frontiers in neurologyReview
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To compare the effectiveness of weak supervision (ie, with examination-level labels only) and strong supervision (ie, with image-level labels) in training deep learning models for detection of intracranial hemorrhage (ICH) on head CT scans. Materials and Methods In this retrospective study, an attention-based convolutional neural network was trained with either local (ie, image level) or global (ie, examination level) binary labels on the Radiological Society of North America (RSNA) 2019 Brain CT Hemorrhage Challenge dataset of 21 736 examinations (8876 [40.8%] ICH) and 752 422 images (107 784 [14.3%] ICH). The CQ500 (436 examinations; 212 [48.6%] ICH) and CT-ICH (75 examinations; 36 [48.0%] ICH) datasets were employed for external testing. Performance in detecting ICH was compared between weak (examination-level labels) and strong (image-level labels) learners as a function of the number of labels available during training. Results On examination-level binary classification, strong and weak learners did not have different area under the receiver operating characteristic curve values on the internal validation split (0.96 vs 0.96;
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