German CheXpert Chest X-ray Radiology Report Labeler
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Abstract (arXiv)
This study aimed to develop an algorithm to automatically extract annotations for chest X-ray classification models from German thoracic radiology reports. An automatic label extraction model was designed based on the CheXpert architecture, and a web-based annotation interface was created for iterative improvements. Results showed that automated label extraction can reduce time spent on manual labeling and improve overall modeling performance. The model trained on automatically extracted labels performed competitively to manually labeled data and strongly outperformed the model trained on publicly available data.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 5 Jun 2023.
Builds on
8 of 28 references.
References · 8
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
- 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 969
- 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 459
- 2023Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classificationcited by 13
- 2022Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamesecited by 5
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.
- 2024Can Rule-Based Insights Enhance LLMs for Radiology Report Classification? Introducing the RadPrompt Methodologycited by 2
- 2023Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learningcited by 0
- 2025MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classificationcited by 0
- 2025MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classification29 Aug 2025
- 2024Can Rule-Based Insights Enhance LLMs for Radiology Report Classification? Introducing the RadPrompt Methodology7 Aug 2024
- 2023Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning9 Jun 2023