Eye tracking (ET) has been used extensively for research in marketing, psychology, and medical image interpretation. It has also been used in medical imaging research to elucidate differences in how expert and novice radiologists interpret images.
Research in Biomedical Imaging Analysis
Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks
Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However, a key barrier in the required training of CNNs is obtaining large-scale and precisely annotated imaging data. We sought to address the lack of annotated data with eye tracking technology.