  Background: Convolutional neural network (CNN)-based melanoma classifiers
face several challenges that limit their usefulness in clinical practice.
Objective: To investigate the impact of multiple real-world dermoscopic views
of a single lesion of interest on a CNN-based melanoma classifier.
  Methods: This study evaluated 656 suspected melanoma lesions. Classifier
performance was measured using area under the receiver operating characteristic
curve (AUROC), expected calibration error (ECE) and maximum confidence change
(MCC) for (I) a single-view scenario, (II) a multiview scenario using multiple
artificially modified images per lesion and (III) a multiview scenario with
multiple real-world images per lesion.
  Results: The multiview approach with real-world images significantly
increased the AUROC from 0.905 (95% CI, 0.879-0.929) in the single-view
approach to 0.930 (95% CI, 0.909-0.951). ECE and MCC also improved
significantly from 0.131 (95% CI, 0.105-0.159) to 0.072 (95% CI: 0.052-0.093)
and from 0.149 (95% CI, 0.125-0.171) to 0.115 (95% CI: 0.099-0.131),
respectively. Comparing multiview real-world to artificially modified images
showed comparable diagnostic accuracy and uncertainty estimation, but
significantly worse robustness for the latter.
  Conclusion: Using multiple real-world images is an inexpensive method to
positively impact the performance of a CNN-based melanoma classifier.
