Resources


Database Credentialed Access

Eye Gaze Data for Chest X-rays

Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou, Joy Wu, Matthew Tong, Arjun Sharma, Shafiq Abedin, David Beymer, Vandana Mukherjee, Elizabeth Krupinski, Mehdi Moradi

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.

convolutional network heatmap eye tracking explainability audio chest cxr chest x-ray radiology machine learning multimodal deep learning

Published: Sept. 12, 2020. Version: 1.0.0


Database Restricted Access

Dataset for Segmentation and Classification of Cardiac Implantable Electronic Devices in Chest X-Rays

Keno Bressem, Felix Busch, Andrei Zhukov, Lisa Adams

This dataset comprises 11,094 converted DICOM and smartphone images of Cardiac Implantable Electronic Devices (CIEDs), collected from 897 patients. It aims to facilitate the development of algorithms for CIED detection and classification.

chest x-ray radiology medical imaging cardiac implantable electronic devices

Published: March 4, 2025. Version: 1.0.0


Database Credentialed Access

MIMIC-Ext-MIMIC-CXR-VQA: A Complex, Diverse, And Large-Scale Visual Question Answering Dataset for 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 introduce MIMIC-Ext-MIMIC-CXR-VQA, a complex, diverse, and large-scale dataset designed for Visual Question Answering (VQA) tasks within the medical domain, focusing primarily on chest radiographs.

question answering chest x-ray radiology electronic health records machine learning multimodal deep learning evaluation visual question answering benchmark

Published: July 19, 2024. Version: 1.0.0


Database Restricted Access

REFLACX: Reports and eye-tracking data for localization of abnormalities in chest x-rays

Ricardo Bigolin Lanfredi, Mingyuan Zhang, William Auffermann, Jessica Chan, Phuong-Anh Duong, Vivek Srikumar, Trafton Drew, Joyce Schroeder, Tolga Tasdizen

This dataset contains 3032 cases of eye-tracking data collected while five radiologists dictated reports for frontal chest x-rays, synchronized timestamped dictation transcription, and manual labels for validation of localization of abnormalities.

eye tracking radiology report reflacx fixations computer vision chest x-rays gaze radiology machine learning deep learning

Published: Sept. 27, 2021. 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 disease progression cxr chest x-ray radiology machine learning multimodal deep learning visual question answering

Published: July 13, 2021. Version: 1.0.0


Database Restricted Access

Dataset for Segmentation and Classification of Cardiac Implantable Electronic Devices in Chest X-Rays

Keno Bressem, Felix Busch, Andrei Zhukov, Lisa Adams

This dataset comprises 11,094 converted DICOM and smartphone images of Cardiac Implantable Electronic Devices (CIEDs), collected from 897 patients. It aims to facilitate the development of algorithms for CIED detection and classification.

chest x-ray radiology medical imaging cardiac implantable electronic devices

Published: March 4, 2025. Version: 1.0.0


Database Credentialed Access

MIMIC-Ext-MIMIC-CXR-VQA: A Complex, Diverse, And Large-Scale Visual Question Answering Dataset for 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 introduce MIMIC-Ext-MIMIC-CXR-VQA, a complex, diverse, and large-scale dataset designed for Visual Question Answering (VQA) tasks within the medical domain, focusing primarily on chest radiographs.

question answering chest x-ray radiology electronic health records machine learning multimodal deep learning evaluation visual question answering benchmark

Published: July 19, 2024. Version: 1.0.0


Database Restricted Access

REFLACX: Reports and eye-tracking data for localization of abnormalities in chest x-rays

Ricardo Bigolin Lanfredi, Mingyuan Zhang, William Auffermann, Jessica Chan, Phuong-Anh Duong, Vivek Srikumar, Trafton Drew, Joyce Schroeder, Tolga Tasdizen

This dataset contains 3032 cases of eye-tracking data collected while five radiologists dictated reports for frontal chest x-rays, synchronized timestamped dictation transcription, and manual labels for validation of localization of abnormalities.

eye tracking radiology report reflacx fixations computer vision chest x-rays gaze radiology machine learning deep learning

Published: Sept. 27, 2021. 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 disease progression cxr chest x-ray radiology machine learning multimodal deep learning visual question answering

Published: July 13, 2021. Version: 1.0.0