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

Understand

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.