Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development.

Journal: Scientific data
Published Date:

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 the eye gaze dataset to show the potential utility of this dataset.

Authors

  • Alexandros Karargyris
    IHU Strasbourg, Strasbourg, France.
  • Satyananda Kashyap
    IBM Research, San Jose, CA, USA.
  • Ismini Lourentzou
  • Joy T Wu
    IBM Almaden Research Center, San Jose, CA.
  • Arjun Sharma
    Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
  • Matthew Tong
    IBM Research, Almaden Research Center, San Jose, CA, 95120, USA.
  • Shafiq Abedin
    IBM Research, Almaden Research Center, San Jose, CA, 95120, USA.
  • David Beymer
    IBM Almaden Research Center, San Jose, CA, USA.
  • Vandana Mukherjee
    IBM Research - Almaden, 650 Harry Rd, San Jose, CA, 95120, USA. vandana@us.ibm.com.
  • Elizabeth A Krupinski
    Department of Radiology and Imaging Sciences, Emory School of Medicine, Atlanta, GA, USA. Electronic address: elizabeth.anne.krupinski@emory.edu.
  • Mehdi Moradi
    IBM Almaden Research Center, San Jose, CA.