  Although highly accurate automated diagnostic techniques for melanoma have
been reported, the realization of a system capable of providing diagnostic
evidence based on medical indices remains an open issue because of difficulties
in obtaining reliable training data. In this paper, we propose bulk production
augmentation (BPA) to generate high-quality, diverse pseudo-skin tumor images
with the desired structural malignant features for additional training images
from a limited number of labeled images. The proposed BPA acts as an effective
data augmentation in constructing the feature detector for the atypical pigment
network (APN), which is a key structure in melanoma diagnosis. Experiments show
that training with images generated by our BPA largely boosts the APN detection
performance by 20.0 percentage points in the area under the receiver operating
characteristic curve, which is 11.5 to 13.7 points higher than that of
conventional CycleGAN-based augmentations in AUC.
