College of Computer Science and Engineering, Lanzhou, Gansu, China.
PLoS One. 2022 Sep 16;17(9):e0267380. doi: 10.1371/journal.pone.0267380. eCollection 2022.
We propose a stacked convolutional neural network incorporating a novel and efficient pyramid residual attention (PRA) module for the task of automatic segmentation of dermoscopic images. Precise segmentation is a significant and challenging step for computer-aided diagnosis technology in skin lesion diagnosis and treatment. The proposed PRA has the following characteristics: First, we concentrate on three widely used modules in the PRA. The purpose of the pyramid structure is to extract the feature information of the lesion area at different scales, the residual means is aimed to ensure the efficiency of model training, and the attention mechanism is used to screen effective features maps. Thanks to the PRA, our network can still obtain precise boundary information that distinguishes healthy skin from diseased areas for the blurred lesion areas. Secondly, the proposed PRA can increase the segmentation ability of a single module for lesion regions through efficient stacking. The third, we incorporate the idea of encoder-decoder into the architecture of the overall network. Compared with the traditional networks, we divide the segmentation procedure into three levels and construct the pyramid residual attention network (PRAN). The shallow layer mainly processes spatial information, the middle layer refines both spatial and semantic information, and the deep layer intensively learns semantic information. The basic module of PRAN is PRA, which is enough to ensure the efficiency of the three-layer architecture network. We extensively evaluate our method on ISIC2017 and ISIC2018 datasets. The experimental results demonstrate that PRAN can obtain better segmentation performance comparable to state-of-the-art deep learning models under the same experiment environment conditions.
我们提出了一种堆叠卷积神经网络，该网络结合了新颖而高效的金字塔残差注意（PRA）模块，用于自动分割皮肤镜图像。精确分割是计算机辅助诊断技术在皮肤病变诊断和治疗中的重要且具有挑战性的步骤。所提出的 PRA 具有以下特征：首先，我们专注于 PRA 中三个广泛使用的模块。金字塔结构的目的是提取病变区域的特征信息，残差表示的目的是确保模型训练的效率，注意力机制用于筛选有效特征图。由于 PRA，我们的网络仍然可以为模糊的病变区域获取区分健康皮肤和病变区域的精确边界信息。其次，所提出的 PRA 可以通过高效堆叠来增加单个模块对病变区域的分割能力。第三，我们将编码器-解码器的思想融入到整个网络的架构中。与传统网络相比，我们将分割过程分为三个层次，并构建金字塔残差注意网络（PRAN）。浅层主要处理空间信息，中层细化空间和语义信息，深层密集学习语义信息。PRAN 的基本模块是 PRA，它足以确保三层架构网络的效率。我们在 ISIC2017 和 ISIC2018 数据集上进行了广泛的评估。实验结果表明，在相同的实验环境条件下，PRAN 可以获得比最先进的深度学习模型更好的分割性能。
