REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays.

ABSTRACT: Deep learning has shown recent success in classifying anomalies in chest x-rays, but datasets are still small compared to natural image datasets. Supervision of abnormality localization has been shown to improve trained models, partially compensating for dataset sizes. However, explicitly labeling these anomalies requires an expert and is very time-consuming. We propose a potentially scalable method for collecting implicit localization data using an eye tracker to capture gaze locations and a microphone to capture a dictation of a report, imitating the setup of a reading room. The resulting REFLACX (Reports and Eye-Tracking Data for Localization of Abnormalities in Chest X-rays) dataset was labeled across five radiologists and contains 3,032 synchronized sets of eye-tracking data and times

SUBMITTER: Bigolin Lanfredi R 

PROVIDER: S-EPMC9206650 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature