  Remarkable progress has been made in image recognition, primarily due to the
availability of large-scale annotated datasets and the revival of deep CNN.
CNNs enable learning data-driven, highly representative, layered hierarchical
image features from sufficient training data. However, obtaining datasets as
comprehensively annotated as ImageNet in the medical imaging domain remains a
challenge. There are currently three major techniques that successfully employ
CNNs to medical image classification: training the CNN from scratch, using
off-the-shelf pre-trained CNN features, and conducting unsupervised CNN
pre-training with supervised fine-tuning. Another effective method is transfer
learning, i.e., fine-tuning CNN models pre-trained from natural image dataset
to medical image tasks. In this paper, we exploit three important, but
previously understudied factors of employing deep convolutional neural networks
to computer-aided detection problems. We first explore and evaluate different
CNN architectures. The studied models contain 5 thousand to 160 million
parameters, and vary in numbers of layers. We then evaluate the influence of
dataset scale and spatial image context on performance. Finally, we examine
when and why transfer learning from pre-trained ImageNet (via fine-tuning) can
be useful. We study two specific computer-aided detection (CADe) problems,
namely thoraco-abdominal lymph node (LN) detection and interstitial lung
disease (ILD) classification. We achieve the state-of-the-art performance on
the mediastinal LN detection, with 85% sensitivity at 3 false positive per
patient, and report the first five-fold cross-validation classification results
on predicting axial CT slices with ILD categories. Our extensive empirical
evaluation, CNN model analysis and valuable insights can be extended to the
design of high performance CAD systems for other medical imaging tasks.
