Resources

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

This is the first Brazilian Multilabel Ophthalmological Dataset with demographic information and retinal photos labeled images according to anatomical parameters, quality control, and presumed diagnosis.

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.

The database contains a corpus of annotated data from the MIMIC-III Critical Care Database from a study that aimed to develop and apply an annotation schema to characterize opioid use disorder and related contextual factors.

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.

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

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

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.

The KURIAS-ECG database is a high-quality 12-lead ECG DB including standard vocabulary (SNOMED CT, OMOP-CDM), and ECG diagnoses of our DB are grouped into 10 diagnoses by applying the minnesota code.

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.