A Convolutional Neural Network Reaches Optimal Sensitivity for Detecting Some, but Not All, Patterns

We investigate the spatial contrast-sensitivity of modern convolutional neural networks (CNNs) and a linear support vector machine (SVM). To measure performance, we compare the CNN contrast sensitivity across a range of patterns with the contrast sensitivity of a Bayesian ideal observer (IO) with the signal-known-exactly and noise-known-statistically. A ResNet-18 reaches optimal performance for harmonic patterns, as well as several classes of...