Resources


Database Credentialed Access

Chest ImaGenome Dataset

Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, Mehdi Moradi

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.

scene graph visual dialogue object detection semantic reasoning bounding box knowledge graph explainability reasoning relation extraction chest cxr disease progression multimodal radiology visual question answering deep learning machine learning chest x-ray

Published: July 13, 2021. Version: 1.0.0

Database Credentialed Access

MS-CXR: Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing

Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel Coelho de Castro, Anton Schwaighofer, Stephanie Hyland, Harshita Sharma, Maria Teodora Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez Valle, Hoifung Poon, Ozan Oktay

MS-CXR is a new dataset containing 1162 chest X-ray bounding box labels paired with radiology text descriptions, annotated and verified by two board-certified radiologists.

vision-language processing localization phrase grounding chest x-ray

Published: Nov. 15, 2024. Version: 1.1.0

Database Credentialed Access

EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images

Seongsu Bae, Daeun Kyung, Jaehee Ryu, Eunbyeol Cho, Gyubok Lee, Sunjun Kweon, Jungwoo Oh, Lei JI, Eric Chang, Tackeun Kim, Edward Choi

We present EHRXQA, the first multi-modal EHR QA dataset combining structured patient records with aligned chest X-ray images. EHRXQA contains a comprehensive set of QA pairs covering image-related, table-related, and image+table-related questions.

question answering benchmark evaluation visual question answering electronic health records multi-modal question answering deep learning ehr question answering semantic parsing machine learning chest x-ray

Published: July 23, 2024. Version: 1.0.0

Database Credentialed Access

EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images

Seongsu Bae, Daeun Kyung, Jaehee Ryu, Eunbyeol Cho, Gyubok Lee, Sunjun Kweon, Jungwoo Oh, Lei JI, Eric Chang, Tackeun Kim, Edward Choi

We present EHRXQA, the first multi-modal EHR QA dataset combining structured patient records with aligned chest X-ray images. EHRXQA contains a comprehensive set of QA pairs covering image-related, table-related, and image+table-related questions.

question answering benchmark evaluation visual question answering electronic health records multi-modal question answering deep learning ehr question answering semantic parsing machine learning chest x-ray

Published: July 23, 2024. Version: 1.0.0

Database Credentialed Access

Chest ImaGenome Dataset

Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, Mehdi Moradi

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.

scene graph visual dialogue object detection semantic reasoning bounding box knowledge graph explainability reasoning relation extraction chest cxr disease progression multimodal radiology visual question answering deep learning machine learning chest x-ray

Published: July 13, 2021. Version: 1.0.0