REFLACX, a dataset of reports and eye-tracking data for localization of
abnormalities in chest x-rays
- URL: http://arxiv.org/abs/2109.14187v1
- Date: Wed, 29 Sep 2021 04:14:16 GMT
- Title: REFLACX, a dataset of reports and eye-tracking data for localization of
abnormalities in chest x-rays
- Authors: Ricardo Bigolin Lanfredi, Mingyuan Zhang, William F. Auffermann,
Jessica Chan, Phuong-Anh T. Duong, Vivek Srikumar, Trafton Drew, Joyce D.
Schroeder, Tolga Tasdizen
- Abstract summary: We propose a method for collecting implicit localization data using an eye tracker to capture gaze locations and a microphone to capture a dictation of a report.
The resulting REFLACX dataset was labeled by five radiologists and contains 3,032 synchronized sets of eye-tracking data and timestamped report transcriptions.
- Score: 22.548782080717096
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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
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, and potentially scalable for large
datasets. The resulting REFLACX (Reports and Eye-Tracking Data for Localization
of Abnormalities in Chest X-rays) dataset was labeled by five radiologists and
contains 3,032 synchronized sets of eye-tracking data and timestamped report
transcriptions. We also provide bounding boxes around lungs and heart and
validation labels consisting of ellipses localizing abnormalities and
image-level labels. Furthermore, a small subset of the data contains readings
from all radiologists, allowing for the calculation of inter-rater scores.
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