Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment
This work addresses breast cancer screening by combining human intuition with deep learning, though it is incremental as it builds on existing methods.
The paper tackled the problem of breast cancer risk assessment by integrating radiologists' rapid 'gist' perception with a pre-trained CNN model, achieving a statistically significant higher AUC than either approach alone.
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.