The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications.

使用自动化方法确定皮肤镜图像中精确的皮肤病变边界面临许多挑战，最重要的是，皮肤镜图像中存在毛发、不显眼的病变边缘和低对比度，以及皮肤病变的颜色、质地和形状的可变性。现有的基于深度学习的皮肤病变分割算法在计算时间和内存方面都很昂贵。因此，运行此类分割算法需要强大的 GPU 和高带宽内存，而这在皮肤镜设备中是不可用的。因此，本文旨在以最少的资源实现精确的皮肤病变分割：一种称为 SLSNet 的轻量级、高效的生成对抗网络 (GAN) 模型，它结合了 1-D 核分解网络、位置和通道注意力，和具有 GAN 模型的多尺度聚合机制。一维核分解网络降低了二维滤波的计算成本。位置和通道注意模块分别增强了空间和通道维度上病变和非病变特征表示之间的区分能力。多尺度块还用于聚合输入皮肤图像的粗到细特征并减少伪影的影响。SLSNet 在两个公开可用的数据集上进行评估：ISBI 2017 和 ISIC 2018。虽然 SLSNet 只有 235 万个参数，但实验结果表明它实现了与最先进的皮肤病变分割方法相媲美的分割结果准确率为 97.61%，Dice 和 Jaccard 相似系数分别为 90.63% 和 81.98%。SLSNet 可以在单个 GTX1080Ti GPU 中以超过 110 帧/秒 (FPS) 的速度运行，这比著名的基于深度学习的图像分割模型（例如 FCN）要快。因此，SLSNet 可用于实际的皮肤镜应用。