Resources

The database is intended to support a wide array of research studies involving radiomics in sepsis patients, helping to reduce barriers to the reproducibility of clinical research.

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.

mBRSET - a Mobile Brazilian Retinal Dataset

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.

1402 kinematic recordings of twenty-two semi-professional actors expressing emotions such as happiness, sadness, anger, fear, disgust, and surprise.

CXLSeg dataset: Chest X-ray with Lung Segmentation, a comparatively large dataset of segmented Chest X-ray radiographs based on the MIMIC-CXR dataset. This contains segmentation results of 243,324 frontal view images and corresponding masks.

Precomputed image embeddings for the BRSET and mBRSET Brazilian retinal datasets to support efficient, secure, and equitable ophthalmic AI research, enabling tasks such as classification, clustering, multimodal modeling, and fairness analysis.

CheXStruct is an automated pipeline that derives structured diagnostic reasoning steps from chest X-rays. CXReasonBench builds on this to evaluate whether models perform clinically grounded, multi-step reasoning beyond final diagnoses.

Endoscapes2023 enables the development of models for object detection, semantic and instance segmentation, and Critical View of Safety (CVS) prediction, contributing to safe laparoscopic cholecystectomy.

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