  Computer-aided diagnosis for low-dose computed tomography (CT) based on deep
learning has recently attracted attention as a first-line automatic testing
tool because of its high accuracy and low radiation exposure. However, existing
methods rely on supervised learning, imposing an additional burden to doctors
for collecting disease data or annotating spatial labels for network training,
consequently hindering their implementation. We propose a method based on a
deep neural network for computer-aided diagnosis called virtual multi-view
projection and reconstruction for unsupervised anomaly detection. Presumably,
this is the first method that only requires data from healthy patients for
training to identify three-dimensional (3D) regions containing any anomalies.
The method has three key components. Unlike existing computer-aided diagnosis
tools that use conventional CT slices as the network input, our method 1)
improves the recognition of 3D lung structures by virtually projecting an
extracted 3D lung region to obtain two-dimensional (2D) images from diverse
views to serve as network inputs, 2) accommodates the input diversity gain for
accurate anomaly detection, and 3) achieves 3D anomaly/disease localization
through a novel 3D map restoration method using multiple 2D anomaly maps. The
proposed method based on unsupervised learning improves the patient-level
anomaly detection by 10% (area under the curve, 0.959) compared with a gold
standard based on supervised learning (area under the curve, 0.848), and it
localizes the anomaly region with 93% accuracy, demonstrating its high
performance.
