Figure 1 |. Deep CNN layout.

Our classification technique is a deep CNN. Data flow is from left to right: an image of a skin lesion (for example, melanoma) is sequentially warped into a probability distribution over clinical classes of skin disease using Google Inception v3 CNN architecture pretrained on the ImageNet dataset (1.28 million images over 1,000 generic object classes) and fine-tuned on our own dataset of 129,450 skin lesions comprising 2,032 different diseases. The 757 training classes are defined using a novel taxonomy of skin disease and a partitioning algorithm that maps diseases into training classes (for example, acrolentiginous melanoma, amelanotic melanoma, lentigo melanoma). Inference classes are more general and are composed of one or more training classes (for example, malignant melanocytic lesions—the class of melanomas). The probability of an inference class is calculated by summing the probabilities of the training classes according to taxonomy structure (see Methods). Inception v3 CNN architecture reprinted from https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html