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

翻译：黑色素瘤的及时治疗至关重要。为了辅助医生快速精准地识别病灶区域，我们提出了一种新型皮肤病变分割技术——SLP-Net，这是一种基于脉冲神经P系统机制的超轻量级分割网络。现有大多数卷积神经网络在实现高分割精度的同时，忽视了高昂的硬件成本。相比之下，SLP-Net具有极少的参数数量和极高的计算速度。我们设计了一种轻量级多尺度特征提取器，摒弃了传统的编码器-解码器结构。取而代之的是，引入了一个特征适配模块来替代解码器，实现多尺度信息解码。在ISIC2018挑战赛上的实验表明，所提模型在现有最优方法中取得了最高的准确率和Dice相似系数，而在PH2数据集上的实验也展现了良好的泛化能力。最后，我们在实验中比较了各模型的计算复杂度与计算速度，其中SLP-Net展现出最高的综合优越性。