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.
Inflammation promotes phenotypic plasticity in melanoma, a source of non-genetic heterogeneity, but the molecular framework is poorly understood.,Here we use functional genomic approaches and identify a reciprocal antagonism between the melanocyte lineage transcription factor MITF and c-Jun, which interconnects inflammation-induced dedifferentiation with pro-inflammatory cytokine responsiveness of melanoma cells favouring myeloid cell recruitment.,We show that pro-inflammatory cytokines such as TNF-α instigate gradual suppression of MITF expression through c-Jun.,MITF itself binds to the c-Jun regulatory genomic region and its reduction increases c-Jun expression that in turn amplifies TNF-stimulated cytokine expression with further MITF suppression.,This feed-forward mechanism turns poor peak-like transcriptional responses to TNF-α into progressive and persistent cytokine and chemokine induction.,Consistently, inflammatory MITFlow/c-Junhigh syngeneic mouse melanomas recruit myeloid immune cells into the tumour microenvironment as recapitulated by their human counterparts.,Our study suggests myeloid cell-directed therapies may be useful for MITFlow/c-Junhigh melanomas to counteract their growth-promoting and immunosuppressive functions.,The c-Jun transcription factor can mediate a cell's response to TNFa.,Here, Riesenberg et al. show in melanoma cells that c-Jun has an inverse relationship with the key melanocyte transcription factor MITF and that high c-Jun levels contribute to melanoma heterogeneity and an inflammatory microenvironment.