Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

Abstract

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that the CNN classifier is superior compared to alternative classification methods based on macro F 1 -scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.

The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review

“…Image transition, i.e., indicating when the current image reading is finished and/or the new image reading starts, is usually controlled by GUI or keyboard button pressing [ 13 ], [ 38 ], [ 62 ], [ 67 ], [ 77 ], [ 78 ], [ 79 ], [ 97 ]. The keyboard-based control is preferred [ 13 ], [ 38 ], [ 62 ], [ 67 ], [ 81 ], [ 97 ] as GUI buttons require the user to move their gaze towards such buttons, which introduces noise to gaze records. The decision recording highly depends on the exact problem of interest.…”

“…The decision recording highly depends on the exact problem of interest. If the decision pool is limited and all decisions are mutually exclusive, they could be recorded using keyboard buttons or mouse clicks [ 21 ], [ 79 ], [ 81 ], [ 97 ]. A more universal approach is to ask physicians to vocalize their decision-making and then parse it manually [ 13 ], [ 14 ], [ 38 ], [ 39 ] or use ML-based speech processing, e.g., Google’s speech-to-text [ 62 ], [ 67 ], [ 98 ].…”

“…For example, a radiologist’s gaze could continue traveling around the image while vocalizing the presence of a small tumor. Complex image enhancements such as zooming are rarely integrated [ 81 ], as they result in changes in the field of view, which is not known to the eye tracker, and therefore require complex gaze data post-processing.…”

“…While gaze heatmaps and fixation points contain valuable information about image reading, they lack a potentially important longitudinal component that encodes the transition between fixation points that is present in gaze paths [35], [37], [46], [50], [51], [75], [81] (Fig. 4b).…”

Classification of Eye Tracking Data in Visual Information Processing Tasks Using Convolutional Neural Networks and Feature Engineering

“…One study focused on the use of deep gaze velocity for biometric identification of radiologist expertise during the reading of mammographic imagery [58]. Individual radiologists were grouped by three levels of expertise including new radiology residents, advanced radiology residents, and expert radiologists.…”