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.
Clonal selection and transcriptional reprogramming (e.g., epithelial-mesenchymal transition or phenotype switching) are the predominant theories thought to underlie tumor progression.,However, a “division of labor” leading to cooperation among tumor-cell subpopulations could be an additional catalyst of progression.,Using a zebrafish-melanoma xenograft model, we found that in a heterogeneous setting, inherently invasive cells, which possess protease activity and deposit extracellular matrix (ECM), co-invade with subpopulations of poorly invasive cells, a phenomenon we term “cooperative invasion”.,Whereas the poorly invasive cells benefit from heterogeneity, the invasive cells switch from protease-independent to an MT1-MMP-dependent mode of invasion.,We did not observe changes in expression of the melanoma phenotype determinant MITF during cooperative invasion, thus ruling out the necessity for phenotype switching for invasion.,Altogether, our data suggest that cooperation can drive melanoma progression without the need for clonal selection or phenotype switching and can account for the preservation of heterogeneity seen throughout tumor progression.,•Inherently invasive and poorly invasive melanoma subpopulations co-invade•Leader cells are inherently invasive and provide MT1-MMP and deposit ECM•Follower cells affect the mode of invasion and thereby elicit protease dependency•Cooperative invasion bypasses phenotype switching and maintains tumor heterogeneity,Inherently invasive and poorly invasive melanoma subpopulations co-invade,Leader cells are inherently invasive and provide MT1-MMP and deposit ECM,Follower cells affect the mode of invasion and thereby elicit protease dependency,Cooperative invasion bypasses phenotype switching and maintains tumor heterogeneity,Tumor heterogeneity has been viewed as the outcome of adaptive competition or differential transcriptional reprogramming (phenotype switching).,Alternatively, heterogeneity may reflect a division of labor between tumor cell subpopulations with complementary acquired characteristics.,Chapman et al. now show reciprocal interactions between heterogeneous melanoma cell subpopulations that lead to cooperative invasion without competition and without phenotype switching.,Thus, cooperative behavior emerges as a property of heterogeneity relevant for tumor biology and worth targeting therapeutically.