Fast Skin Lesion Segmentation via Fully Convolutional Network with Residual Architecture and CRF

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 Network(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.