Predicting Radiologists’ Gaze with Computational Saliency Models in Mammogram Reading
Previous studies have shown that there is a strong correlation between radiologists’ diagnoses and their gaze when reading medical images. The extent to which gaze is attracted by content in a visual scene can be characterised as visual saliency. There is a potential for the use of visual saliency in computer-aided diagnosis in radiology. However, little is known about what methods are effective for diagnostic images, and how these methods could be adapted to address specific applications in diagnostic imaging. In this study, we investigate 20 state-of-the-art saliency models including 10 traditional models and 10 deep learning-based models in predicting radiologists’ visual attention while reading 196 mammograms. We found that deep learning-based models represent the most effective type of methods for predicting radiologists’ gaze in mammogram reading; and that the performance of these saliency models can be significantly improved by transfer learning. In particular, an enhanced model
