PDF(Pubmed)
Abstract：
In clinical conditions limited by equipment, attaining lightweight skin lesion segmentation is pivotal as it facilitates the integration of the model into diverse medical devices, thereby enhancing operational efficiency. However, the lightweight design of the model may face accuracy degradation, especially when dealing with complex images such as skin lesion images with irregular regions, blurred boundaries, and oversized boundaries. To address these challenges, we propose an efficient lightweight attention network (ELANet) for the skin lesion segmentation task. In ELANet, two different attention mechanisms of the bilateral residual module (BRM) can achieve complementary information, which enhances the sensitivity to features in spatial and channel dimensions, respectively, and then multiple BRMs are stacked for efficient feature extraction of the input information. In addition, the network acquires global information and improves segmentation accuracy by putting feature maps of different scales through multi-scale attention fusion (MAF) operations. Finally, we evaluate the performance of ELANet on three publicly available datasets, ISIC2016, ISIC2017, and ISIC2018, and the experimental results show that our algorithm can achieve 89.87%, 81.85%, and 82.87% of the mIoU on the three datasets with a parametric of 0.459 M, which is an excellent balance between accuracy and lightness and is superior to many existing segmentation methods.
摘要：
在受设备限制的临床条件下，实现轻量级的皮肤病变分割至关重要，因为它有助于将模型集成到各种医疗设备中，从而提高运营效率。然而,模型的轻量化设计可能面临精度下降，特别是当处理复杂的图像，如皮肤病变图像与不规则区域，模糊的边界,和超大的边界。为了应对这些挑战，我们提出了一个有效的轻量级注意网络（ELANet）用于皮肤病变分割任务。在ELANet,两种不同的注意机制的双边残差模块(BRM)可以实现信息互补,这增强了对空间和通道维度特征的敏感性，分别,然后将多个BRM堆叠起来，对输入信息进行有效的特征提取。此外,该网络通过多尺度注意力融合（MAF）操作放置不同尺度的特征图来获取全局信息并提高分割精度。最后,我们评估了ELANet在三个公开可用数据集上的性能，ISIC2016、ISIC2017和ISIC2018,实验结果表明,我们的算法可以达到89.87%,81.85%,三个参数为0.459M的数据集上的mIoU的82.87%，这是一个很好的平衡之间的准确性和亮度，是优于许多现有的分割方法。
