Convolutional neural network model observers discount signal-like anatomical structures during search in virtual...

We aim to assess the perceptual tasks in which convolutional neural networks (CNNs) might be better tools than commonly used linear model observers (LMOs) to evaluate medical image quality.

We compared the LMOs (channelized Hotelling [CHO] and frequency convolution channels observers [FCO]) and CNN detection accuracies for tasks with a few possible signal locations (location known exactly) and for the search for mass and microcalcification signals embedded in 2D/3D breast tomosynthesis phantoms. We also compared the LMOs and CNN accuracies to those of radiologists in the search tasks. We analyzed radiologists' eye position to assess whether they fixate longer at locations considered suspicious by the LMOs or those by the CNN.

LMOs resulted in similar detection accuracies [area under the receiver operating characteristic curve (AUC)] to the CNN for tasks with up to 100 signal locations but lower accuracies in the search task for microcalcification and mass 3D images. Radiologists' AUC was significantly higher ( $p<1e-4$ ) than that of LMOs for the microcalcification 2D search (CHO, FCO) and 3D mass search ( $p<0.05$ , CHO) but was not higher than the CNN's AUC. For both signal types, radiologists fixated longer on the locations of the highest response scores of the CNN than those of the LMOs but only reached statistical significance for the mass (masses: $p=0.009$ versus CHO and $p=0.004$ versus FCO).

We show that CNNs are a more suitable model observer for search tasks. Like radiologists but not traditional LMOs, CNNs can discount false positives arising from anatomical backgrounds.