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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.

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

Published: March 20, 2024. Version: 1.0.0

Database Credentialed Access

MIMIC-CXR-JPG - chest radiographs with structured labels

Alistair Johnson, Matthew Lungren, Yifan Peng, et al.

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

computer vision chest x-ray radiology mimic deep learning

Published: March 12, 2024. Version: 2.1.0

Database Credentialed Access

RadCoref: Fine-tuning coreference resolution for different styles of clinical narratives

Yuxiang Liao, Hantao Liu, Irena Spasic

RadCoref is a small subset of MIMIC-CXR with manually annotated coreference mentions and clusters. Based on the annotated data, we fine-tuned a deep neural model and used it to annotate the whole MIMIC-CXR dataset. Both data are available.

natural language processing coreference resolution radiology

Published: Jan. 30, 2024. Version: 1.0.0

Database Restricted Access

LATTE-CXR: Locally Aligned TexT and imagE, Explainable dataset for Chest X-Rays

Elham Ghelichkhan, Tolga Tasdizen

This dataset includes bounding box-statement pairs for chest X-ray images, derived from radiologists’ eye-tracking data (for explainability) and annotations, for local visual-language models.

eye-tracking chest x-ray dataset automatically generated dataset caption-guided object detection image captioning with region-level description grounded radiology report generation phrase grounding xai multi-modal learning local visual-language models localization

Published: Feb. 4, 2025. Version: 1.0.0

Database Contributor Review

COVID Data for Shared Learning (CDSL): A comprehensive, multimodal COVID-19 dataset from HM Hospitales

Álvaro Ritoré, Andreea M Oprescu, Alberto Estirado Bronchalo, et al.

COVID Data for Shared Learning (CDSL) is a multimodal database comprising de-identified structured health data and radiological images from 4,479 patients with COVID-19, as a comprehensive toolkit for developing predictive models.

covid-19 multimodal database radiological images open data healthcare data machine learning and ai

Published: Oct. 25, 2024. Version: 1.0.0

Database Credentialed Access

MIMIC-CXR Database

Alistair Johnson, Tom Pollard, Roger Mark, et al.

Chest radiographs in DICOM format with associated free-text reports.

computer vision chest x-rays natural language processing machine learning radiology mimic

Published: July 23, 2024. Version: 2.1.0

Database Credentialed Access

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

Shruthi Bannur, Stephanie Hyland, Qianchu Liu, et al.

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 chest x-ray radiology multimodal

Published: March 17, 2023. Version: 1.0.0

Database Credentialed Access

Eye Gaze Data for Chest X-rays

Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou, et al.

This dataset was a collected using an eye tracking system while a radiologist interpreted and read 1,083 public CXR images. The dataset contains the following aligned modalities: image, transcribed report text, dictation audio and eye gaze data.

convolutional network heatmap eye tracking explainability audio chest cxr machine learning chest x-ray radiology multimodal deep learning

Published: Sept. 12, 2020. Version: 1.0.0

Model Credentialed Access

RadVLM model

Nicolas Deperrois, Hidetoshi Matsuo, Samuel Ruiperez-Campillo, et al.

RadVLM is a 7B-parameter vision-language model fine-tuned on public chest-X-ray data that drafts reports, lists abnormalities, grounds findings, and chats about a CXR through a single image-to-text interface.

Published: Oct. 8, 2025. Version: 1.0.0

Database Open Access

ReXErr-v1: Clinically Meaningful Chest X-Ray Report Errors Derived from MIMIC-CXR

Vishwanatha Rao, Serena Zhang, Julian Acosta, et al.

Chest X-Ray reports containing synthetic errors based upon the MIMIC-CXR database. Errors were injected using LLMs and sampled across common human and AI model errors.

Published: March 19, 2025. Version: 1.0.0