Resources
15 results for "structured radiology reports"
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CXRGraph: Using Information Extraction to Normalize the Training Data for Automatic Radiology Report Generation
CXRGraph is a dataset of structured radiology reports dataset following the RadGraph format, which has been tailored for the Automatic Radiology Report Generation (ARRG) task. CXRGraph assorts clinical information from full-text radiology reports in…
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RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports
Radiology reports are essential for clinical care but pose challenges for automated processing due to their unstructured nature. Existing datasets like RadGraph-1.0 focus narrowly on chest X-rays (CXR), limiting their applicability. We introduce Rad…
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RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
RadGraph is a dataset of entities and relations in full-text radiology reports. We designed a novel information extraction (IE) schema to structure clinical information in a radiology report with four entities and three relations. Our train set cons…
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Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation
Radiology reports convey detailed clinical observations and capture diagnostic reasoning that evolves over time. However, existing evaluation methods are limited to single-report settings and rely on coarse metrics that fail to capture fine-grained …
fine-grained structured reports
attribute-level clinical reasoning
medical text structuring
longitudinal clinical reasoning
chest x-ray report parsing
medical information structuring
benchmark dataset for radiology report
medical information extraction
structured radiology reports
temporal relation extraction
radiology report benchmarking
longitudinal clinical understanding
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MIMIC-CXR-JPG - chest radiographs with structured labels
The MIMIC Chest X-ray JPG (MIMIC-CXR-JPG) Database v2.0.0 is a large publicly available dataset of chest radiographs in JPG format with structured labels derived from free-text radiology reports. The MIMIC-CXR-JPG dataset is wholly derived from MIMI…
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Pulmonary Edema Severity Grades Based on MIMIC-CXR
Clinical management decisions for patients with acutely decompensated heart failure and many other diseases are often based on grades of pulmonary edema severity, rather than its mere absence or presence. Chest radiographs are commonly performed to …
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Application of Med-PaLM 2 in the refinement of MIMIC-CXR labels
MIMIC-CXR is a large, open source dataset that is widely-used in medical AI research. One of the limitations of this dataset is the lack of ground truth labels for the chest X-ray studies. Prior work has extracted structured labels from the MIMIC-CX…
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Swiss-Mammo: A physician-written, synthetic dataset of German mammography reports
This dataset, Swiss-Mammo, contains 28 manually constructed German mammography reports, each paired with an English translation. The reports are stratified across BI-RADS categories 0 through 6, with four reports per category. All reports were manua…
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MIMIC-Ext-CXR-QBA: A Structured, Tagged, and Localized Visual Question Answering Dataset with Question-Box-Answer Triplets and Scene Graphs for Chest X-ray Images
Visual Question Answering (VQA) enables flexible and context-dependent analysis of medical images, such as chest X-rays (CXRs), by allowing users to pose specific questions and receive nuanced answers. However, existing CXR VQA datasets are typicall…
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LLaVA-Rad MIMIC-CXR Annotations
LLaVA-Rad MIMIC-CXR features more accurate section extractions from MIMIC-CXR free-text radiology reports. Traditionally, rule-based methods were used to extract sections such as the reason for exam, findings, and impression. However, these approach…