Resources

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.

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.

VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations

CheXmask Database is a 657,566 uniformly annotated chest radiographs with segmentation masks. Images were segmented using HybridGNet, with automatic quality control indicated by RCA scores.

We present a large-scale CXR VQA dataset derived from MIMIC-CXR with 42M QA pairs, featuring hierarchical answers, bounding boxes, and structured tags. We generated QA-pairs using LLM-based extraction from radiology reports and localization models.

A multimodal clinical dataset consisting of CXRs, ECGs, and blood labs, designed to evaluate Symile, a simple contrastive loss that accommodates any number of modalities and allows any model to produce representations for each modality.

A chest x-rays segmentation dataset derived from MIMIC-CXR based on deep learning algorithm and human examination.

CXR-LT 2024 was a challenge for long-tailed, multi-label, and zero-shot thorax disease classification on chest X-rays, held at MICCAI 2024. This page contains long-tailed labels for 45 diseases from the CXR-LT 2024 and 2023 challenges.

We present EHRXQA, the first multi-modal EHR QA dataset combining structured patient records with aligned chest X-ray images. EHRXQA contains a comprehensive set of QA pairs covering image-related, table-related, and image+table-related questions.

VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations