  Model observers are computational tools to evaluate and optimize task-based
medical image quality. Linear model observers, such as the Channelized
Hotelling Observer (CHO), predict human accuracy in detection tasks with a few
possible signal locations in clinical phantoms or real anatomic backgrounds. In
recent years, Convolutional Neural Networks (CNNs) have been proposed as a new
type of model observer. What is not well understood is what CNNs add over the
more common linear model observer approaches. We compare the CHO and CNN
detection accuracy to the radiologist's accuracy in searching for two types of
signals (mass and microcalcification) embedded in 2D/3D breast tomosynthesis
phantoms (DBT). We show that the CHO model's accuracy is comparable to the
CNN's performance for a location-known-exactly detection task. However, for the
search task with 2D/3D DBT phantoms, the CHO's detection accuracy was
significantly lower than the CNN accuracy. A comparison to the radiologist's
accuracy showed that the CNN but not the CHO could match or exceed the
radiologist's accuracy in the 2D microcalcification and 3D mass search
conditions. An analysis of the eye position showed that radiologists fixated
more often and longer at the locations corresponding to CNN false positives.
Most CHO false positives were the phantom's normal anatomy and were not fixated
by radiologists. In conclusion, we show that CNNs can be used as an
anthropomorphic model observer for the search task for which traditional linear
model observers fail due to their inability to discount false positives arising
from the anatomical backgrounds.
