  We investigate the effectiveness of a simple solution to the common problem
of deep learning in medical image analysis with limited quantities of labeled
training data. The underlying idea is to assign artificial labels to abundantly
available unlabeled medical images and, through a process known as surrogate
supervision, pre-train a deep neural network model for the target medical image
analysis task lacking sufficient labeled training data. In particular, we
employ 3 surrogate supervision schemes, namely rotation, reconstruction, and
colorization, in 4 different medical imaging applications representing
classification and segmentation for both 2D and 3D medical images. 3 key
findings emerge from our research: 1) pre-training with surrogate supervision
is effective for small training sets; 2) deep models trained from initial
weights pre-trained through surrogate supervision outperform the same models
when trained from scratch, suggesting that pre-training with surrogate
supervision should be considered prior to training any deep 3D models; 3)
pre-training models in the medical domain with surrogate supervision is more
effective than transfer learning from an unrelated domain (e.g., natural
images), indicating the practical value of abundant unlabeled medical image
data.
