Resources

CARMEN-I is a Spanish corpus of 2,000 clinical records from Hospital Clínic, Barcelona. It covers COVID-19 patients and comorbidities, serving as a resource for training clinical NLP models and researchers in NLP applied to clinical documents.

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.

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.

A corpus of cytology and histopathology reports annotated for terminology relevant to fungal infections. Ideal for validation of named entity recognition and relation extraction methods.

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.

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

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.

Deidentified free-text clinical notes for patients in the MIMIC-IV Clinical Database.

BRAX contains 24,959 chest radiography exams and 40,967 images acquired in a large general Brazilian hospital. All images have been read by trained radiologists and 14 labels were derived from Brazilian Portuguese reports using NLP.

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.