Time-Interval Visual Saliency Prediction in Mammogram Reading

Jianxun Lou, Xinbo Wu, Richard White, Yingying Wu, Hantao Liu

Abstract: Radiologists’ eye movements during medical image interpretation reflect their perceptual-cognitive behaviour and correlate with diagnostic decisions. Previous study has shown the significance of gaze behaviour of different time intervals for the decision-making process. Being able to automatically predict the visual attention of radiologists for different reading phases would enhance the reliability and explainability of artificial intelligence (AI) in diagnostic imaging. In this paper, we investigate the time-interval visual saliency in mammogram reading. We propose a novel visual saliency prediction model based on deep learning, which predicts a sequence of time-interval saliency maps for an input mammogram. Experimental results demonstrate the efficacy of the proposed time-interval saliency model.