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Modeling visual search behavior of breast radiologists using a deep convolution neural network.

Suneeta Mall¹, Patrick C Brennan¹, Claudia Mello-Thoms¹

¹University of Sydney, Faculty of Health Sciences, Medical Image Optimisation and Perception Research Group (MIOPeG), Lidcombe, New South Wales, Australia.

Journal of Medical Imaging (Bellingham, Wash.)

|August 22, 2018

Summary

This study models radiologist visual search behavior during mammogram interpretation using deep learning. The model accurately predicts radiologist decisions and confidence levels, aiding in understanding diagnostic errors.

Keywords:

behavior modeling breast cancer deep learning eye tracking machine learning mammography visual search

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Area of Science:

Medical Imaging
Artificial Intelligence
Cognitive Science

Background:

Mammogram interpretation errors can stem from visual search patterns.
Understanding radiologist visual behavior is crucial for improving diagnostic accuracy.

Purpose of the Study:

To model radiologist visual search behavior and mammogram interpretation using deep machine learning.
To correlate visual attention with diagnostic decisions and confidence levels.

Main Methods:

A deep convolutional neural network model was developed, incorporating transfer learning.
Eye-tracking data from eight radiologists interpreting 120 mammography cases were used for training.
Visual search maps were analyzed to categorize areas of mammograms by visual attention (foveal, peripheral, none).

Main Results:

The model achieved high accuracy in predicting radiologists' decisions and confidence.
Low misclassification rates were observed in modeling visual search and interpretation behaviors.
The model successfully linked visual attention levels to diagnostic outcomes.

Conclusions:

Deep learning models can effectively simulate radiologist visual search and decision-making in mammography.
This approach offers insights into diagnostic processes and potential error sources.
The findings support the use of AI in analyzing and potentially enhancing radiological interpretation.