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

arXiv: https://arxiv.org/abs/2002.06588 · HTML: https://mlsanity.com/paper/2002.06588/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models](https://mlsanity.com/paper/2105.00827/) (2021)
- [Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT](https://mlsanity.com/paper/2304.04920/) (2023)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification](https://mlsanity.com/paper/2403.04024/) (2024)
- [Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotation](https://mlsanity.com/paper/2206.14305/) (2022)
- [RadEx: A Framework for Structured Information Extraction from Radiology Reports based on Large Language Models](https://mlsanity.com/paper/2406.15465/) (2024)
- [A Systematic Review of Natural Language Processing Applied to Radiology Reports](https://mlsanity.com/paper/2102.09553/) (2021)
- [Automated triaging of head MRI examinations using convolutional neural networks](https://mlsanity.com/paper/2106.08176/) (2021)
- [Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique](https://mlsanity.com/paper/2209.12177/) (2022)
- [Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection](https://mlsanity.com/paper/2210.09698/) (2022)
- [Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach](https://mlsanity.com/paper/2309.02681/) (2023)
- [A Dual-View Approach to Classifying Radiology Reports by Co-Training](https://mlsanity.com/paper/2406.05995/) (2024)
- [PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling](https://mlsanity.com/paper/2605.20052/) (2026)

## 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.