The Lifecycle of A Neural Network in The Wild: A Multiple instance Learning Study On Cancer Detection From Breast Biopsies Imaged With Novel Technique

It is apparent that humans are intrinsically capable of determining the degree of complexity present in an image; but it is unclear which regions in that image lead humans towards evaluating an image as complex or simple. Here, we develop a novel deep learning model for predicting human perception of the complexity of natural scene images in order to address these problems. For a given image, our approach, ComplexityNet, can generate both single-score complexity ratings and two-dimensional per-pixel complexity maps. These complexity maps indicate the regions of scenes that humans find to be complex, or simple. Drawing on work in the cognitive sciences we integrate metrics for scene clutter and scene symmetry, and conclude that the proposed metrics do indeed boost neural network performance when predicting complexity.