Resources


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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

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

Visual Question Answering evaluation dataset for MIMIC CXR

Timo Kohlberger, Charles Lau, Tom Pollard, Andrew Sellergren, Atilla Kiraly, Fayaz Jamil

This dataset provides 224 VQAs for 40 test set cases, and 111 VQAs for 23 validation set cases of the MIMIC CXR dataset.

Published: Jan. 28, 2025. Version: 1.0.0

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 Credentialed Access

CAD-Chest: Comprehensive Annotation of Diseases based on MIMIC-CXR Radiology Report

Mengliang Zhang, Xinyue Hu, Lin Gu, Tatsuya Harada, Kazuma Kobayashi, Ronald Summers, Yingying Zhu

The CAD-Chest dataset provides comprehensive annotations of disease, including disease severity, uncertainty, and location based on the MIMIC-CXR radiologist reports.

chesr x-ray disease label

Published: Dec. 8, 2023. Version: 1.0

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

RadNLI: A natural language inference dataset for the radiology domain

Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, Dan Jurafsky

A radiology NLI dataset introduced in the paper: Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation

Published: June 29, 2021. Version: 1.0.0

Database Credentialed Access

Medical-CXR-VQA dataset: A Large-Scale LLM-Enhanced Medical Dataset for Visual Question Answering on Chest X-Ray Images

Xinyue Hu, Lin Gu, Kazuma Kobayashi, liangchen liu, Mengliang Zhang, Tatsuya Harada, Ronald Summers, Yingying Zhu

Medical-CXR-VQA provides a large-scale LLM-enhanced dataset for visual question answering in medical chest x-ray images.

Published: Jan. 21, 2025. Version: 1.0.0

Database Credentialed Access

Chest ImaGenome Dataset

Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, Mehdi Moradi

The Chest ImaGenome dataset is a scene graph dataset with additional chronological comparison relations for chest X-rays. It is automatically derived from the MIMIC-CXR dataset. A manually annotated gold standard is also available for 500 patients.

scene graph visual dialogue object detection semantic reasoning bounding box knowledge graph explainability reasoning relation extraction chest disease progression cxr radiology deep learning chest x-ray machine learning multimodal visual question answering

Published: July 13, 2021. Version: 1.0.0

Database Credentialed Access

MS-CXR: Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing

Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel Coelho de Castro, Anton Schwaighofer, Stephanie Hyland, Harshita Sharma, Maria Teodora Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez Valle, Hoifung Poon, Ozan Oktay

MS-CXR is a new dataset containing 1162 chest X-ray bounding box labels paired with radiology text descriptions, annotated and verified by two board-certified radiologists.

vision-language processing chest x-ray localization phrase grounding

Published: Nov. 15, 2024. Version: 1.1.0