Automated Melanocytic Lesion Classification: Capsule Networks Trained With Synthetic Images Can Outperform Networks Trained With Real Images

Since the effectiveness of automated melanocytic lesion image classification is limited by the difficulty in obtaining large numbers of high-quality training data, our results support the idea that training a classifier utilising synthetic images-where large numbers of quality images can be easily generated-is inherently advantageous. Given its advantages over a CNN, and trained on a very large number of synthetic images of melanocytic lesions, a CN classifier thus has the potential, only limited by computer resources, to yield previously unseen levels of generalisation performance.