Skin lesions are a severe disease globally.,Early detection of melanoma in dermoscopy images significantly increases the survival rate.,However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc.,Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists.,In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3).,A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result.,A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map.,A straight-forward CNN is proposed for the dermoscopic feature extraction task.,The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset.,Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.
People with pale skin, red hair, freckles, and an inability to tan-the “redhair/fairskin” phenotype- are at highest risk of developing melanoma, compared to all other pigmentation types1.,Genetically, this phenotype is frequently the product of inactivating polymorphisms in the Melanocortin 1 receptor (MC1R) gene.,MC1R encodes a cAMP stimulating G-protein coupled receptor that controls pigment production.,Minimal receptor activity, as in redhair/fairskin polymorphisms, produces red/yellow pheomelanin pigment, while increasing MC1R activity stimulates production of black/brown eumelanin2.,Pheomelanin has weak UV shielding capacity relative to eumelanin and has been shown to amplify UVA-induced reactive oxygen species (ROS) 3-5.,Several observations, however, complicate the assumption that melanoma risk is completely UV dependent.,For example, unlike non-melanoma skin cancers, melanoma is not restricted to sun-exposed skin and UV signature mutations are infrequently oncogenic drivers6.,While linkage of melanoma risk to UV exposure is beyond doubt, UV-independent events are also likely to play a significant role1,7.,Here, we introduced into mice carrying an inactivating mutation in the Mc1r gene (who exhibit a phenotype analogous to redhair/fairskin humans), a conditional, melanocyte-targeted allele of the most commonly mutated melanoma oncogene, BRafV600E.,We observed a high incidence of invasive melanomas without providing additional gene aberrations or UV exposure.,To investigate the mechanism of UV-independent carcinogenesis, we introduced an albino allele, which ablates all pigment production on the Mc1r e/e background.,Selective absence of pheomelanin synthesis was protective against melanoma development.,In addition, normal Mc1re/e mouse skin was found to have significantly greater oxidative DNA and lipid damage than albino-Mc1re/e mouse skin.,These data suggest that the pheomelanin pigment pathway produces UV-independent carcinogenic contributions to melanomagenesis by a mechanism of oxidative damage.,While UV protection remains important, additional strategies may be required for optimal melanoma prevention.