SLP-Net:An efficient lightweight network for segmentation of skin lesions
This addresses the need for efficient, real-time segmentation of melanoma lesions to assist physicians, though it appears incremental as it builds on existing lightweight network concepts.
The paper tackled skin lesion segmentation by proposing SLP-Net, an ultra-lightweight network that achieved the highest accuracy and Dice score on the ISIC2018 challenge while maintaining high computational speed and low hardware cost.
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