Resources

Visual Question Answering (VQA) enables flexible and context-dependent analysis of medical images, such as chest X-rays (CXRs), by allowing users to pose specific questions and receive nuanced answers. However, existing CXR VQA datasets are typicall…

Interpreting medical images and writing radiology reports is a critical yet challenging task in healthcare. Despite their importance, both human-written and AI-generated reports are liable to errors, leaving a need for robust and representative data…

Cardiomegaly is a condition characterized by an abnormal enlargement of the heart, its identification is of paramount importance as it associate with a wide range of cardiac conditions. It is primary identified via the cardiothoracic ratio (CTR), ho…

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…

Segmenting the heart and lungs from chest X-ray images is essential for accurate disease diagnosis, enabling the calculation of image-derived digital biomarkers for cardiopulmonary health assessment. Furthermore, creation of annotated data sets can …

As more and more artificial intelligence (AI) or deep learning technologies have been applied to medical image applications such as radiological finding identification in chest X-rays (CXRs), the interpretability of the prediction model is crucial f…

Several extant chest X-ray (CXR) datasets predominantly comprise binary disease labels and exhibit a deficiency in providing comprehensive disease-related information. Crucial facets of disease management, including disease severity, diagnostic unce…

RadGraph is a dataset of entities and relations in full-text radiology reports. We designed a novel information extraction (IE) schema to structure clinical information in a radiology report with four entities and three relations. Our train set cons…

Chest X-ray(CXR) images are prominent among medical images and are commonly administered in emergency diagnosis and treatment corresponding to cardiac and respiratory diseases. Though there are robust solutions available for medical diagnosis, valid…

Symile-MIMIC is a multimodal clinical dataset derived from MIMIC-IV and MIMIC-CXR, consisting of chest X-rays (CXRs), electrocardiograms (ECGs), and blood laboratory tests. It was developed to evaluate Symile, a contrastive learning objective design…