  Automated computer-aided detection (CADe) in medical imaging has been an
important tool in clinical practice and research. State-of-the-art methods
often show high sensitivities but at the cost of high false-positives (FP) per
patient rates. We design a two-tiered coarse-to-fine cascade framework that
first operates a candidate generation system at sensitivities of $\sim$100% but
at high FP levels. By leveraging existing CAD systems, coordinates of regions
or volumes of interest (ROI or VOI) for lesion candidates are generated in this
step and function as input for a second tier, which is our focus in this study.
In this second stage, we generate $N$ 2D (two-dimensional) or 2.5D views via
sampling through scale transformations, random translations and rotations with
respect to each ROI's centroid coordinates. These random views are used to
train deep convolutional neural network (ConvNet) classifiers. In testing, the
trained ConvNets are employed to assign class (e.g., lesion, pathology)
probabilities for a new set of $N$ random views that are then averaged at each
ROI to compute a final per-candidate classification probability. This second
tier behaves as a highly selective process to reject difficult false positives
while preserving high sensitivities. The methods are evaluated on three
different data sets with different numbers of patients: 59 patients for
sclerotic metastases detection, 176 patients for lymph node detection, and
1,186 patients for colonic polyp detection. Experimental results show the
ability of ConvNets to generalize well to different medical imaging CADe
applications and scale elegantly to various data sets. Our proposed methods
improve CADe performance markedly in all cases. CADe sensitivities improved
from 57% to 70%, from 43% to 77% and from 58% to 75% at 3 FPs per patient for
sclerotic metastases, lymph nodes and colonic polyps, respectively.
