The prevalence of skin melanoma is rapidly increasing as well as the recorded death cases of its patients. Automatic image segmentation tools play an important role in providing standardized computer-assisted analysis for skin melanoma patients. Current state-of-the-art segmentation methods are based on fully convolutional neural networks, which utilize an encoder-decoder approach. However, these methods produce coarse segmentation masks due to the loss of location information during the encoding layers. Inspired by Pyramid Scene Parsing Network (PSP-Net), we propose an encoder-decoder model that utilizes pyramid pooling modules in the deep skip connections which aggregate the global context and compensate for the lost spatial information. We trained and validated our approach using ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection grand challenge dataset. Our approach showed a validation accuracy with a Jaccard index of 0.837, which outperforms U-Net. We believe that with this reported reliable accuracy, this method can be introduced for clinical practice.

本研究提出一种利用金字塔池化模块的编码-解码神经网络模型，通过深度跳跃连接聚合全局上下文并补偿丢失的空间信息，以提供标准化的皮肤黑色素瘤患者计算机辅助分析，并通过 ISIC 2018 数据集的训练和验证，在 Jaccard 系数上取得了0.837验证准确性，优于 U-Net，相信这种方法可以用于临床实践。

基于聚合多分辨率跳跃连接的深度卷积编码器-解码器用于皮肤病变分割