Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment

TL;DR

This study demonstrates that combining radiologists' rapid gist impressions with deep learning models significantly improves breast cancer risk assessment accuracy, surpassing individual gist perception or models alone.

Contribution

It introduces a novel integration of human gist perception with CNN models, enhancing breast cancer risk prediction performance.

Findings

Gist input improves CNN model AUC significantly.

Radiologists' gist perception alone exceeds chance in classification.

Combined model outperforms individual gist or CNN alone.

Abstract

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.