  Purpose: Radiologists are tasked with visually scrutinizing large amounts of
data produced by 3D volumetric imaging modalities. Small signals can go
unnoticed during the 3d search because they are hard to detect in the visual
periphery. Recent advances in machine learning and computer vision have led to
effective computer-aided detection (CADe) support systems with the potential to
mitigate perceptual errors.
  Approach: Sixteen non-expert observers searched through digital breast
tomosynthesis (DBT) phantoms and single cross-sectional slices of the DBT
phantoms. The 3D/2D searches occurred with and without a convolutional neural
network (CNN)-based CADe support system. The model provided observers with
bounding boxes superimposed on the image stimuli while they looked for a small
microcalcification signal and a large mass signal. Eye gaze positions were
recorded and correlated with changes in the area under the ROC curve (AUC).
  Results: The CNN-CADe improved the 3D search for the small microcalcification
signal (delta AUC = 0.098, p = 0.0002) and the 2D search for the large mass
signal (delta AUC = 0.076, p = 0.002). The CNN-CADe benefit in 3D for the small
signal was markedly greater than in 2D (delta delta AUC = 0.066, p = 0.035).
Analysis of individual differences suggests that those who explored the least
with eye movements benefited the most from the CNN-CADe (r = -0.528, p =
0.036). However, for the large signal, the 2D benefit was not significantly
greater than the 3D benefit (delta delta AUC = 0.033, p = 0.133).
  Conclusion: The CNN-CADe brings unique performance benefits to the 3D (vs.
2D) search of small signals by reducing errors caused by the under-exploration
of the volumetric data.
