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.
Abstract---This is a work-in-progress report of a research endeavor that deals with the design and development of a novel approach to computer-aided diagnosis (CAD) of mammographic images. With the initial emphasis being on the analysis of microcalcifications, the proposed approach defines a synergistic paradigm that utilizes new methodologies together with previously developed techniques. The new paradigm is intended to promote a higher degree of accuracy in CAD of mammogram with an increased overall throughput. The process of accomplishing these goals is initiated by the fractal encoding of the input image, which gives rise to the generation of focus-of-attention regions (FA%), that is, regions that contain anomalies. The primary thrust of this work is to demonstrate that by considering FA%, rather than the entire input image, the performances of the ensuing processes (i.e., segmentation, feature extraction, and classification) are enhanced in term of accuracy and speed. After presenting the proposed approach to CAD of mammographic images, the paper describes the generation of FA&. Furthermore, an experimental study is included that demonstrates the impact of this front-end procedure on the process of microcalcification segmentation. Specifically, the experimentation reveals a dramatic decrease (increase) in the amount of input data (throughput), as well as a reduction in the number of false detections.
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