TITLE: Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development 

ABSTRACT: We developed a rich dataset of Chest X-Ray (CXR) images to assist
investigators in artificial intelligence. The data were collected using an eye
tracking system while a radiologist reviewed and reported on 1,083 CXR images.
The dataset contains the following aligned data: CXR image, transcribed
radiology report text, radiologist's dictation audio and eye gaze coordinates
data. We hope this dataset can contribute to various areas of research
particularly towards explainable and multimodal deep learning / machine
learning methods. Furthermore, investigators in disease classification and
localization, automated radiology report generation, and human-machine
interaction can benefit from these data. We report deep learning experiments
that utilize the attention maps produced by eye gaze dataset to show the
potential utility of this data.
We developed a rich dataset of Chest X-Ray (CXR) images to assist
investigators in artificial intelligence. The data were collected using an eye
tracking system while a radiologist reviewed and reported on 1,083 CXR images.
The dataset contains the following aligned data: CXR image, transcribed
radiology report text, radiologist's dictation audio and eye gaze coordinates
data. We hope this dataset can contribute to various areas of research
particularly towards explainable and multimodal deep learning / machine
learning methods. Furthermore, investigators in disease classification and
localization, automated radiology report generation, and human-machine
interaction can benefit from these data. We report deep learning experiments
that utilize the attention maps produced by eye gaze dataset to show the
potential utility of this data.
Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development
http://arxiv.org/abs/2009.07386v3