Resources
7 results for "multi-modal learning"
Database
Credentialed Access
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images
Electronic Health Records (EHRs), which contain patients' medical histories in various multi-modal formats, often overlook the potential for joint reasoning across imaging and table modalities underexplored in current EHR Question Answering (QA) sys…
Database
Credentialed Access
Eye Gaze Data for Chest X-rays
We created a rich multimodal dataset for the Chest X-Ray (CXR) domain. The data was 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…
Database
Contributor Review
A multimodal dental dataset facilitating machine learning research and clinic services
Oral diseases affect nearly 3.5 billion people, with the majority residing in low- and middle-income countries. Due to limited healthcare resources, many individuals are unable to access proper oral healthcare services. Image-based machine learning …
Database
Credentialed Access
MIMIC-Ext-MIMIC-CXR-VQA: A Complex, Diverse, And Large-Scale Visual Question Answering Dataset for Chest X-ray Images
We introduce MIMIC-Ext-MIMIC-CXR-VQA (i.e., Extended from MIMIC database), a complex, diverse, and large-scale dataset designed for Visual Question Answering (VQA) tasks within the medical domain, focusing primarily on chest radiographs. This datase…
Database
Credentialed Access
MS-CXR-T: Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
MS-CXR-T is a multi-modal benchmark dataset for evaluating biomedical vision-language processing (VLP) models on two distinct temporal tasks in radiology: image classification and sentence similarity. The former comprises multi-image frontal chest X…
Database
Credentialed Access
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
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…
Database
Restricted Access
LATTE-CXR: Locally Aligned TexT and imagE, Explainable dataset for Chest X-Rays
Local annotation of medical data is both expensive and time-consuming due to the high cost of expert annotators, the precision required for accurate annotation, and the inherent challenges of medical diagnosis. To address these problems, we develop…