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.
Malignant melanoma is a rare disease in Asia, and knowledge on its characteristics and clinical outcome in Asian patients is limited.,The purpose of this observational study was to determine the clinical presentation and outcome of patients with melanoma in China.,A database was prospectively established for the purpose of this analysis.,The elements of the database included basic demographic data of patients and prognosticators previously reported in literature, as well as follow-up data including clinical outcome after treatment.,Medical record of all patients with pathologically diagnosed malignant melanoma consulted in our center since 2006 were retrieved and reviewed.,No patient was excluded in this study.,Statistical analyses including survival and multivariate analyses of factors associated with survival were respectively performed by Kaplan-Meier method and Cox proportional hazard model.,A total of 522 consecutive and nonselected cases were evaluated.,There were 218 cases (41.8%) of acral lentiginous melanoma (ALM), 118 (22.6%) of mucosal melanoma (MCM), 103 (19.7%) of nodular melanoma (NM), 33 (6.3%) of superficial spreading melanoma (SSM), and others were Lentigo maligna melanoma or unclassifiable disease.,The proportion of patients with clinical stage I, II, III, and IV diseases were 6.1%, 55.9%, 25.1%, and 12.8%, respectively.,Among the 357 cases of cutaneous melanoma, 234 patients (65.5%) had ulceration.,The 5-year overall survival rate of all 522 patients was 41.6%, and the median survival time was 3.92 years (95% CI, 3.282 to 4.558).,Five-year survival rates of patients with stage I, II, III, and IV diseases were 94.1%, 44.0%, 38.4% and 4.6% respectively (P < 0.001).,Multivariate analysis revealed that clinical stage and the ulceration were two significant prognosticators for OS.,In addition, extent of surgery and use of adjuvant therapy were significant prognosticators for DFS in patients with non-metastatic disease after definitive treatment.,Pathological subtype was not a significant prognostic factor to predict wither OS or DFS.,Prognoses of patients with malignant melanoma diagnosed in China were suboptimal, and most patients were diagnosed with locally advanced disease (i.e., stage II or above).,ALM and MCM are the two most commonly diagnosed pathological subtypes.,Clinical staging and presence of ulceration was significantly associated with clinical outcome in terms of OS, while treatment strategy including extent of surgery and use of adjuvant therapy were significant predictors of DFS.