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This manuscript describes our participation in the International Skin ImagingCollaboration's 2017 Skin Lesion Analysis Towards Melanoma Detectioncompetition. We participated in Part 3: Lesion Classification. The two statedgoals of this binary image classification challenge were to distinguish between(a) melanoma and (b) nevus and seborrheic keratosis, followed by distinguishingbetween (a) seborrheic keratosis and (b) nevus and melanoma. We chose a deepneural network approach with a transfer learning strategy, using a pre-trainedInception V3 network as both a feature extractor to provide input for amulti-layer perceptron as well as fine-tuning an augmented Inception network.This approach yielded validation set AUC's of 0.84 on the second task and 0.76on the first task, for an average AUC of 0.80. We joined the competitionunfortunately late, and we look forward to improving on these results.

Transfer Learning for Melanoma Detection: Participation in ISIC 2017 Skin Lesion Classification Challenge