Skin cancer is the most common cancer world-wide, among which Melanoma the most fatal cancer, accounts for more than 10,000 deaths annually in Australia and United States. The 5-year survival rate for Melanoma can be increased over 90% if detected in its early stage. However, intrinsic visual similarity across various skin conditions makes the diagnosis challenging both for clinicians and automated classification methods. Many automated skin cancer diagnostic systems have been proposed in literature, all of which consider solely dermoscopy images in their analysis. In reality, however, clinicians consider two modalities of imaging; an initial screening using clinical photography images to capture a macro view of the mole, followed by dermoscopy imaging which visualizes morphological structures within the skin lesion. Evidences show that these two modalities provide complementary visual features that can empower the decision making process. In this work, we propose a novel deep convolutional neural network (DCNN) architecture along with a saliency feature descriptor to capture discriminative features of the two modalities for skin lesions classification. The proposed DCNN accepts a pair images of clinical and dermoscopic view of a single lesion and is capable of learning single-modality and cross-modality representations, simultaneously. Using one of the largest collected skin lesion datasets, we demonstrate that the proposed multi-modality method significantly outperforms single-modality methods on three tasks; differentiation between 15 various skin diseases, distinguishing cancerous (3 cancer types including melanoma) from non-cancerous moles, and detecting melanoma from benign cases.
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