A Multi-scale and Multi-attention Network for Skin Lesion Segmentation

Accurately segmenting the diseased areas from dermoscopy images is highly meaningful for the diagnosis of skin cancer, and in recent years, methods based on deep convolutional neural networks have become the mainstream for automatic segmentation of skin lesions. Although these methods have made significant improvements in the field of skin lesion segmentation, capturing long-range dependencies remains a major challenge for convolutional neural networks. In order to address this limitation, this paper proposes a deep learning model for skin lesion segmentation called the Multi-Scale and Multi-Attention Network (MSMANet). The encoder part utilizes a pretrained ResNet for feature extraction. In the skip connection part, we adopt a novel non-local method called the Fully Attentional Block (FLA), which effectively obtains long-range contextual information and retains attentions in all dimensions. In the decoder part, we propose a multi-attention decoder that consists of four attention modules, allowing effective attention to be given to the feature maps in three dimensions: spatial, channel, and scale. We conducted experiments on two publicly available skin lesion segmentation datasets, ISIC 2017 and ISIC 2018, and the results demonstrate that MSMA-Net outperforms other methods, confirming the effectiveness of MSMA-Net.