Abstract
Melanoma is known to be the most fatal form of skin cancers. In order to achieve automated diagnosis of such disease, a system is needed to accurately locate suspicious skin lesions using images captured by standard digital cameras. Recently, there exists a trend for the use of Fully Convolutional Net-work(FCN) to perform image segmentation task. In this paper, we propose a FCN-based processing pipeline that incorporates a deep neural net and a graphical model, to attain a segmentation mask of lesion region from normal skin. Our method extends the residual network by adding a transposed convolution layer to yield a FCN architecture. We demonstrate that the noisy outcome from FCN can be refined by a fully connected Conditional Random Field(CRF). Our model enjoys three major advantages over existing algorithms: simpler process pipeline, state-of-art accuracy in terms of segmentation sensitivity(95.6%) and fast inference time.