Prompt treatment for melanoma is crucial. To assist physicians in identifying lesion areas precisely in a quick manner, we propose a novel skin lesion segmentation technique namely SLP-Net, an ultra-lightweight segmentation network based on the spiking neural P(SNP) systems type mechanism. Most existing convolutional neural networks achieve high segmentation accuracy while neglecting the high hardware cost. SLP-Net, on the contrary, has a very small number of parameters and a high computation speed. We design a lightweight multi-scale feature extractor without the usual encoder-decoder structure. Rather than a decoder, a feature adaptation module is designed to replace it and implement multi-scale information decoding. Experiments at the ISIC2018 challenge demonstrate that the proposed model has the highest Acc and DSC among the state-of-the-art methods, while experiments on the PH2 dataset also demonstrate a favorable generalization ability. Finally, we compare the computational complexity as well as the computational speed of the models in experiments, where SLP-Net has the highest overall superiority

我们提出了一种新颖的皮肤病变分割技术SLP-Net，它是一种基于脉冲神经P(SNP)系统类型机制的超轻量级分割网络，具有很少的参数和高速计算的特点。在ISIC2018挑战赛中，我们的模型在准确率和DSC指标上都达到了最高水平，而在PH2数据集上也展现了良好的泛化能力。与其他模型相比，SLP-Net在计算复杂性和计算速度上具有明显的优势。

SLP-Net: 皮肤病病变分割的高效轻量级网络