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Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)

arXiv 2002.06588 ↗First posted 16 Feb 2020cs.CVcited by 14

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Abstract (arXiv)

Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label their own MRI datasets for medical imaging applications.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 4; 2025: 0; 2026: 1.

14 papers.

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  1. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  2. 2021AMMU : A Survey of Transformer-based Biomedical Pretrained Language Modelscited by 32
  3. 2023Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPTcited by 28
  4. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
  5. 2024Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classificationcited by 8
  6. 2022Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotationcited by 1
  7. 2024RadEx: A Framework for Structured Information Extraction from Radiology Reports based on Large Language Modelscited by 1
  8. 2021A Systematic Review of Natural Language Processing Applied to Radiology Reportscited by 0
  9. 2021Automated triaging of head MRI examinations using convolutional neural networkscited by 0
  10. 2022Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Techniquecited by 0
  11. 2022Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detectioncited by 0
  12. 2023Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approachcited by 0
2 more
  1. 2024A Dual-View Approach to Classifying Radiology Reports by Co-Trainingcited by 0
  2. 2026PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labelingcited by 0