  Skin lesion segmentation is an important step for automatic melanoma
diagnosis. Due to the non-negligible diversity of lesions from different
patients, extracting powerful context for fine-grained semantic segmentation is
still challenging today. Although the deep convolutional neural network (CNNs)
have made significant improvements on skin lesion segmentation, they often fail
to reserve the spatial details and long-range dependencies context due to
consecutive convolution striding and pooling operations inside CNNs. In this
paper, we formulate a cascaded context enhancement neural network for automatic
skin lesion segmentation. A new cascaded context aggregation (CCA) module with
a gate-based information integration approach is proposed to sequentially and
selectively aggregate original image and multi-level features from the encoder
sub-network. The generated context is further utilized to guide discriminative
features extraction by the designed context-guided local affinity (CGL) module.
Furthermore, an auxiliary loss is added to the CCA module for refining the
prediction. In our work, we evaluate our approach on four public skin
dermoscopy image datasets. The proposed method achieves the Jaccard Index (JA)
of 87.1%, 80.3%, 83.4%, and 86.6% on ISIC-2016, ISIC-2017, ISIC-2018, and PH2
datasets, which are higher than other state-of-the-art models respectively.
