Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment
Skylar W. Wurster, Arkadiusz Sitek, Jian Chen, Karla K. Evans, Gaeun Kim, Jeremy M. Wolfe · arXiv (Cornell University) · 2019
Radiologists can classify a mammogram as normal or abnormal at better than chance levels after less than a second's exposure to the images. In this work, we combine these radiologists' gist inputs into pre-trained machine learning models to validate that integrating gist with a CNN model can achieve an AUC (area under the curve) statistically significantly higher than either the gist perception of radiologists or the model without gist input.