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CXRGraph: Using Information Extraction to Normalize the Training Data for Automatic Radiology Report Generation

Yuxiang Liao, Hoisang Heung, Hantao Liu, Irena Spasic

CXRGraph is a structured radiology report dataset built upon RadGraph and tailored for the Automatic Radiology Report Generation task. It can identify more task-relevant information such as abnormalities and hallucinated prior references.

relation extraction information extraction natural language processing named entity recognition structured radiology report

Published: Feb. 3, 2025. Version: 1.0.0

Database Restricted Access

CXRGraph: Using Information Extraction to Normalize the Training Data for Automatic Radiology Report Generation

Yuxiang Liao, Hoisang Heung, Hantao Liu, Irena Spasic

CXRGraph is a structured radiology report dataset built upon RadGraph and tailored for the Automatic Radiology Report Generation task. It can identify more task-relevant information such as abnormalities and hallucinated prior references.

relation extraction information extraction natural language processing named entity recognition structured radiology report

Published: Feb. 3, 2025. Version: 1.0.0

Database Credentialed Access

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven QH Truong, Du Nguyen Duong, Tan Bui, Pierre Chambon, Matthew Lungren, Andrew Ng, Curtis Langlotz, Pranav Rajpurkar

RadGraph is a dataset of entities and relations in full-text chest X-ray radiology reports, which are obtained using a novel information extraction (IE) schema to capture clinically relevant information in a radiology report.

entity and relation extraction graph multi-modal natural language processing radiology

Published: June 3, 2021. Version: 1.0.0

Database Credentialed Access

MIMIC-CXR-JPG - chest radiographs with structured labels

Alistair Johnson, Matthew Lungren, Yifan Peng, Zhiyong Lu, Roger Mark, Seth Berkowitz, Steven Horng

Chest x-rays in JPG format with structured labels derived from the associated radiology report.

computer vision radiology deep learning chest x-ray mimic

Published: March 12, 2024. Version: 2.1.0

Database Restricted Access

Pulmonary Edema Severity Grades Based on MIMIC-CXR

Ruizhi Liao, Geeticka Chauhan, Polina Golland, Seth Berkowitz, Steven Horng

Pulmonary edema metadata and labels for MIMIC-CXR

Published: Feb. 9, 2021. Version: 1.0.1

Database Restricted Access

Application of Med-PaLM 2 in the refinement of MIMIC-CXR labels

Kendall Park, Rory Sayres, Andrew Sellergren, Tom Pollard, Fayaz Jamil, Timo Kohlberger, Charles Lau, Atilla Kiraly

This work further refines the labels associated with CheXpert in MIMIC-CXR-JPG 2.0.0 by filtering with Med-PaLM 2 followed by verification by manual review by three US board-certified radiologists.

mimic-cxr labels

Published: Feb. 4, 2025. Version: 1.0.0

Database Credentialed Access

LLaVA-Rad MIMIC-CXR Annotations

Juan Manuel Zambrano Chaves, Shih-Cheng Huang, Yanbo Xu, Hanwen Xu, Naoto Usuyama, Sheng Zhang, Fei Wang, Yujia Xie, Mahmoud Khademi, Ziyi Yang, Hany Awadalla, Julia Gong, Houdong Hu, Jianwei Yang, Chunyuan Li, Jianfeng Gao, Yu Gu, Cliff Wong, Mu-Hsin Wei, Tristan Naumann, Muhao Chen, Matthew Lungren, Akshay Chaudhari, Serena Yeung, Curtis Langlotz, Sheng Wang, Hoifung Poon

This dataset provides GPT-4 extracted sections of radiology reports from MIMIC-CXR, complementing rule-based section extractions with additional reports with findings, and removing references to priors from findings.

Published: Jan. 24, 2025. Version: 1.0.0

Database Credentialed Access

CHIFIR: Cytology and Histopathology Invasive Fungal Infection Reports

Vlada Rozova, Anna Khanina, Jasmine Teng, Joanne Teh, Leon Worth, Monica Slavin, karin thursky, Karin Verspoor

A corpus of cytology and histopathology reports annotated for terminology relevant to fungal infections. Ideal for validation of named entity recognition and relation extraction methods.

nlp clinical documentation information extraction invasive fungal infections

Published: Feb. 20, 2024. Version: 1.0.2

Database Restricted Access

CheXchoNet: A Chest Radiograph Dataset with Gold Standard Echocardiography Labels

Pierre Elias, Shreyas Bhave

Early detection of heart failure is vital for improving outcomes. The dataset contains 71,589 CXRs paired with gold standard labels from echocardiograms to enable the training of models to detect pathologies indicative of early stage heart failure.

heart failure chest x-rays early detection cardiac structural abnormalties deep learning

Published: March 20, 2024. Version: 1.0.0

Database Credentialed Access

MS-CXR-T: Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

Shruthi Bannur, Stephanie Hyland, Qianchu Liu, Fernando Pérez-García, Max Ilse, Daniel Coelho de Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anton Schwaighofer, Maria Teodora Wetscherek, Hannah Richardson, Tristan Naumann, Javier Alvarez Valle, Ozan Oktay

The MS-CXR-T is a multimodal benchmark that enhances the MIMIC-CXR v2 dataset by including expert-verified annotations. Its goal is to evaluate biomedical visual-language processing models in terms of temporal semantics extracted from image and text.

disease progression cxr vision-language processing radiology chest x-ray multimodal

Published: March 17, 2023. Version: 1.0.0