论文标题
自动皮肤病变细分的级联上下文增强网络
Cascaded Context Enhancement Network for Automatic Skin Lesion Segmentation
论文作者
论文摘要
皮肤病变分割是自动黑色素瘤诊断的重要步骤。由于来自不同患者的病变的不可忽略的多样性，为细粒度的语义分割提取强大的环境仍然具有挑战性。尽管深度卷积神经网络（CNN）已对皮肤病变细分进行了重大改进，但由于连续卷积的步骤和汇总CNN内部的操作，它们通常无法保留空间细节和远程依赖环境。在本文中，我们为自动皮肤病变细分制定了级联的上下文增强神经网络。提出了一种具有基于门的信息集成方法的新的级联上下文聚合（CCA）模块，以从编码器子网络中顺序和选择性地汇总原始图像和多级特征。生成的上下文进一步用于指导设计的上下文引导的本地亲和力（CGL）模块提取区分特征。此外，将辅助损失添加到CCA模块中以完善预测。在我们的工作中，我们评估了四个公共皮肤皮肤镜图像数据集的方法。所提出的方法在ISIC-2016，ISIC-2017，ISIC-2018，ISIC-2018和PH2数据集上，Jaccard指数（JA）为87.1％，80.3％，83.4％和86.6％，它们分别高于其他最佳型号。
Skin lesion segmentation is an important step for automatic melanoma diagnosis. Due to the non-negligible diversity of lesions from different patients, extracting powerful context for fine-grained semantic segmentation is still challenging today. Although the deep convolutional neural network (CNNs) have made significant improvements on skin lesion segmentation, they often fail to reserve the spatial details and long-range dependencies context due to consecutive convolution striding and pooling operations inside CNNs. In this paper, we formulate a cascaded context enhancement neural network for automatic skin lesion segmentation. A new cascaded context aggregation (CCA) module with a gate-based information integration approach is proposed to sequentially and selectively aggregate original image and multi-level features from the encoder sub-network. The generated context is further utilized to guide discriminative features extraction by the designed context-guided local affinity (CGL) module. Furthermore, an auxiliary loss is added to the CCA module for refining the prediction. In our work, we evaluate our approach on four public skin dermoscopy image datasets. The proposed method achieves the Jaccard Index (JA) of 87.1%, 80.3%, 83.4%, and 86.6% on ISIC-2016, ISIC-2017, ISIC-2018, and PH2 datasets, which are higher than other state-of-the-art models respectively.
