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Because of their state-of-the-art performance in computer vision, CNNs arebecoming increasingly popular in a variety of fields, including medicine.However, as neural networks are black box function approximators, it isdifficult, if not impossible, for a medical expert to reason about theiroutput. This could potentially result in the expert distrusting the networkwhen he or she does not agree with its output. In such a case, explaining whythe CNN makes a certain decision becomes valuable information. In this paper,we try to open the black box of the CNN by inspecting and visualizing thelearned feature maps, in the field of dermatology. We show that, to someextent, CNNs focus on features similar to those used by dermatologists to makea diagnosis. However, more research is required for fully explaining theiroutput.

Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification