Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment

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.

Predicting Visual Memory Schemas with Variational Autoencoders

Visual memory schema (VMS) maps show which regions of an image cause that image to be remembered or falsely remembered. Previous work has succeeded in generating low resolution VMS maps using convolutional neural networks. We instead approach this problem as an image-to-image translation task making use of a variational autoencoder. This approach allows us to generate higher resolution dual channel images that represent visual memory schemas, allowing us to evaluate predicted true memorability and false memorability separately. We also evaluate the relationship between VMS maps, predicted VMS maps, ground truth memorability scores, and predicted memorability scores.