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.
The incidence of malignant melanoma is increasing faster than that for any other cancer.,Histological examination of skin excision biopsies remains the standard method for melanoma diagnosis and prognosis.,Significant morphological overlap between benign and malignant lesions complicates diagnosis, and tumour thickness is not always an accurate predictor of prognosis.,To identify improved molecular markers to support histological examination, we used microarray analysis of formalin-fixed and paraffin-embedded samples from different stages of melanomagenesis to identify differentially expressed microRNAs (miRNAs).,Differential expression was validated by qRT-PCR, and functional studies were carried out after transfection of miRNA precursors or inhibitors into melanoma cells to modulate miRNA expression.,In all, 20 miRNAs showed highly significant differential expression between benign naevi and either primary or metastatic melanomas, the majority being downregulated in melanoma, whereas only 2 miRNAs, namely miR-203 and miR-205, were differentially expressed between primary and metastatic melanomas.,In functional in vitro assays, overexpression of miR-200c and miR-205 inhibited anchorage-independent colony formation and overexpression of miR-211 inhibited both anchorage-independent colony formation and invasion.,We have identified a series of differentially expressed miRNAs that could be useful as diagnostic or prognostic markers for melanoma and have shown that three miRNAs (namely miR-200c, miR-205 and miR-211) act as tumour suppressors.