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。为帮助医生快速精确地识别病灶区域，我们提出了一种新颖的皮肤病变分割技术SLP-Net，这是一种基于脉冲神经P系统（SNP）机制的超轻量级分割网络。现有大多数卷积神经网络虽能达到高分割精度，却忽略了高昂的硬件成本。相反，SLP-Net参数量极小且计算速度快。我们设计了一种轻量级多尺度特征提取器，摒弃了常规的编码器-解码器结构。替代解码器的是特征自适应模块，该模块负责实现多尺度信息解码。在ISIC2018挑战赛上的实验表明，所提模型在准确率（Acc）和戴斯相似系数（DSC）上均优于现有最优方法；在PH2数据集上的实验同样验证了其良好的泛化能力。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，SLP-Net展现出全面的优越性。