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ACFNet: An Adaptive Context Fusion Network for Skin Lesion Segmentation

Skin lesion segmentation is a key step in computer-aided diagnosis. Fully convolution network-based approaches have achieved great results. However, inadequate feature extraction has lost many important features as well as rough fusion has introduced many adverse noises. All of these limit the accuracy of skin lesion segmentation. In this paper, we propose a new and effective adaptive context fusion network for skin lesion segmentation. The proposed network is based on the U-Net architecture. An adaptive context fusion module (ACF) is used to extract more adequate feature. Combining the advantages of atrous convolution and attention mechanism, it can not only reduce the loss of spatial information, but also further refine the extracted feature. The gated residual fusion module (GRF) is added to skip architecture to make the fusion process more refined. It can suppress invalid information in the fusion process with less spatial information loss. We evaluate the proposed method on two benchmark datasets: ISIC 2016 and ISIC 2017, and the experimental results show that the proposed method achieves significant accuracy improvement compared with the existing several networks. The ablation experiments also prove the effectiveness of the proposed two modules.