  Background: As available medical image datasets increase in size, it becomes
infeasible for clinicians to review content manually for knowledge extraction.
The objective of this study was to create an automated clustering resulting in
human-interpretable pattern discovery.
  Methods: Images from the public HAM10000 dataset, including 7 common
pigmented skin lesion diagnoses, were tiled into 29420 tiles and clustered via
k-means using neural network-extracted image features. The final number of
clusters per diagnosis was chosen by either the elbow method or a compactness
metric balancing intra-lesion variance and cluster numbers. The amount of
resulting non-informative clusters, defined as those containing less than six
image tiles, was compared between the two methods.
  Results: Applying k-means, the optimal elbow cutoff resulted in a mean of
24.7 (95%-CI: 16.4-33) clusters for every included diagnosis, including 14.9%
(95% CI: 0.8-29.0) non-informative clusters. The optimal cutoff, as estimated
by the compactness metric, resulted in significantly fewer clusters (13.4;
95%-CI 11.8-15.1; p=0.03) and less non-informative ones (7.5%; 95% CI: 0-19.5;
p=0.017). The majority of clusters (93.6%) from the compactness metric could be
manually mapped to previously described dermatoscopic diagnostic patterns.
  Conclusions: Automatically constraining unsupervised clustering can produce
an automated extraction of diagnostically relevant and human-interpretable
clusters of visual patterns from a large image dataset.
