Ismini Lourentzou


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.


Ismini Lourentzou

Publication Details

Date of publication:
September 15, 2020
Cornell University
Publication note:

Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou, Joy T. Wu, Arjun Sharma, Matthew Tong, Shafiq Abedin, David Beymer, Vandana Mukherjee, Elizabeth A. Krupinski, Mehdi Moradi: Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development. CoRR abs/2009.07386 (2020)