  We explore several oversampling techniques for an imbalanced multi-label
classification problem, a setting often encountered when developing models for
Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim to
optimize classifiers for overall accuracy without considering the relative
distribution of each class, we look into using synthetic sampling to increase
per-class performance when predicting the degree of malignancy. Using low-level
image features and a random forest classifier, we show that using synthetic
oversampling techniques increases the sensitivity of the minority classes by an
average of 7.22% points, with as much as a 19.88% point increase in sensitivity
for a particular minority class. Furthermore, the analysis of low-level image
feature distributions for the synthetic nodules reveals that these nodules can
provide insights on how to preprocess image data for better classification
performance or how to supplement the original datasets when more data
acquisition is feasible.
