German CheXpert Chest X-ray Radiology Report Labeler
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.
Code (0)
등록된 구현이 없습니다.
Tasks
X-ray ClassificationSimilar Papers 제목 키워드 기반
Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation
Although deep learning models for chest X-ray interpretation are commonly trained on labels generated by automatic radiology report labelers, the impact of improvements in report labeling on the performance of chest X-ra…
ClassificationGeneral Classificationimage-classificationImage Classification+1Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning
Radiologists are in short supply globally, and deep learning models offer a promising solution to address this shortage as part of clinical decision-support systems. However, training such models often requires expensive…
Deep LearningCheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output
It is often infeasible or impossible to obtain ground truth labels for medical data. To circumvent this, one may build rule-based or other expert-knowledge driven labelers to ingest data and yield silver labels absent an…
Active LearningDiagnosticRadGraph: Extracting Clinical Entities and Relations from Radiology Reports
Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In our work, we present RadGraph, a datase…
Relation ExtractionCheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 …
Lung Disease Classification