  Skin lesion is a severe disease in world-wide extent. 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, e.g. 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. International Skin Imaging Collaboration (ISIC) is
a challenge focusing on the automatic analysis of skin lesion. In this paper,
we proposed two deep learning methods to address all the three tasks announced
in ISIC 2017, 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.
To our best knowledges, we are not aware of any previous work proposed for this
task. The proposed deep learning frameworks were evaluated on the ISIC 2017
testing set. Experimental results show the promising accuracies of our
frameworks, i.e. 0.718 for task 1, 0.833 for task 2 and 0.823 for task 3 were
achieved.
