The incidence of skin cancer, which has high mortality, is growing rapidly worldwide. Early detection of skin lesions is crucial for timely diagnosis and treatment to improve the patient survival rate. Computer vision technology based on deep convolutional neural network requires a large amount of labelled data. The cost of data acquisition and annotation is relatively high, especially for skin cancer segmentation tasks. Therefore, we propose a few-shot segmentation network for skin lesion segmentation, which requires only a few pixel-level annotations. First, the co-occurrence region between the support image and query image is obtained, which is used as a prior mask to exclude irrelevant background regions. Second, the results are concatenated and sent to the inference module to predict segmentation of the query image. Third, the proposed network is retrained by reversing the support and query role, which benefits from the symmetrical structure. Extensive experiments performed on ISIC-2017, ISIC-2019, and PH2 demonstrate that our method forms a promising framework for few-shot segmentation of skin lesion.

死亡率高的皮肤癌的发病率在世界范围内迅速增长。早期发现皮肤病变对于及时诊断和治疗以提高患者存活率至关重要。基于深度卷积神经网络的计算机视觉技术需要大量的标记数据。数据获取和注释的成本相对较高，尤其是对于皮肤癌分割任务。因此，我们提出了用于皮肤病变分割的几次镜头分割网络，该网络仅需要几个像素级注释。首先，获得支持图像和查询图像之间的共现区域，将其用作先验遮罩以排除无关的背景区域。其次，将结果串联起来并发送到推理模块，以预测查询图像的分割。第三，所提议的网络通过反转支持和查询角色进行重新训练，这得益于对称结构。在ISIC-2017，ISIC-2019和PH2上进行的大量实验表明，我们的方法为皮肤病变的少量镜头分割提供了有希望的框架。