Resources

MIMIC-CXR is a large, open source dataset that is widely-used in medical AI research. One of the limitations of this dataset is the lack of ground truth labels for the chest X-ray studies. Prior work has extracted structured labels from the MIMIC-CX…

The MUSIC (MUerte Subita en Insuficiencia Cardiaca) study is a prospective, multicentre, longitudinal study designed to assess risk predictors of cardiac mortality and sudden cardiac death (SCD) in ambulatory patients with chronic heart failure (CHF…

Automatic medical report generation (MRG) towards describing life-threatening lesions from given medical images, such as Chest X-ray and Fundus Fluorescein Angiography (FFA), has been a long-standing research topic in machine learning and automatic …

We release a new dataset, MS-CXR, with locally-aligned phrase grounding annotations by board-certified radiologists to facilitate the study of complex semantic modelling in biomedical vision–language processing. The MS-CXR dataset provides 1162 imag…

COVID Data for Shared Learning (CDSL) is a multimodal database comprising de-identified medical data from 4,479 patients who were hospitalized with confirmed or suspected COVID-19 in the Spanish 'HM Hospitales' group from 2019-12-26 to 2021-02-13. T…

Generative vision-language models have exciting potential implications for radiology report generation, but unfortunately such models are also known to produce hallucinations and other nonsensical statements. For example, radiology report generation…

RadGraph2 is a dataset of 800 chest radiology reports annotated using a fine-grained entity-relationship schema, which is an expanded version of the previously introduced RadGraph dataset. In contrast with the previous approaches and the original Ra…

Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology. Such a human-in-the-loop radiology assistant could facilitate a collaborative diagnostic …

Existing datasets containing physiological data are mostly acquired at rest or in controlled scenarios. As a result, algorithms developed using such data may not perform as well as with biosignals acquired in dynamic and uncontrolled environments. S…

A trial was conducted to collect gauge pressure and flow from a venturi-based flow-meter, dynamic abdominal and thoracic circumference from rotary-encoder based tape measures, and aeration data from electrical impedance tomography (EIT). Data was co…