PCF-Net: Position and context information fusion attention convolutional neural network for skin lesion segmentation

Skin lesion segmentation is a crucial step in the process of skin cancer diagnosis and treatment. The variation in position, shape, size and edges of skin lesion areas poses a challenge for accurate segmentation of skin lesion areas through dermoscopic images. To meet these challenges, in this paper, using UNet as the baseline model, a convolutional neural network based on position and context information fusion attention is proposed, called PCF-Net. A novel two-branch attention mechanism is designed to aggregate Position and Context information, called Position and Context Information Aggregation Attention Module (PCFAM). A global context information complementary module (GCCM) was developed to obtain long-range dependencies. A multi-scale grouped dilated convolution feature extraction module (MSEM) was proposed to capture multi-scale feature information and place it in the bottleneck of UNet. On the ISIC2018 dataset, a large volume of ablation experiments demonstrated the superiority of PCF-Net for dermoscopic image segmentation after adding PCFAM, GCCM and MSEM. Compared with other state-of-the-art methods, the performance of PCF-Net achieves a competitive result in all metrics.