论文标题
互补网络，具有适应性接受领域的黑色素瘤分割
Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation
论文作者
论文摘要
皮肤镜图像中的自动黑色素瘤分割对于皮肤癌的计算机辅助诊断至关重要。现有方法可能会遭受漏洞和收缩问题的损失，而细分性能有限。为了解决这些问题，我们提出了一个新颖的互补网络，并通过自适应接受学习。我们不用独立就分割任务进行分段任务，而是引入了一个前景网络，以检测黑色素瘤病变和掩盖非黑色素瘤区域的背景网络。此外，我们提出自适应的非常屈服（AAC）和知识聚集模块（KAM），以填充孔并减轻收缩问题。 AAC通过具有自适应接收场的扩张卷积，以多个尺度和KAM Recolves浅层图明确控制接受场，并根据深度特征地图进行调整。此外，提出了一种新型的相互损失来利用前景网络和背景网络之间的依赖性，从而在这两个网络中产生了相互影响。因此，这种相互训练策略可以使半监督的学习并改善边界敏感性。通过皮肤成像协作（ISIC）2018皮肤病变细分数据集进行培训，我们的方法达到了86.4％的骰子，并且与最先进的黑色素瘤细分方法相比，表现更好。
Automatic melanoma segmentation in dermoscopic images is essential in computer-aided diagnosis of skin cancer. Existing methods may suffer from the hole and shrink problems with limited segmentation performance. To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning. Instead of regarding the segmentation task independently, we introduce a foreground network to detect melanoma lesions and a background network to mask non-melanoma regions. Moreover, we propose adaptive atrous convolution (AAC) and knowledge aggregation module (KAM) to fill holes and alleviate the shrink problems. AAC explicitly controls the receptive field at multiple scales and KAM convolves shallow feature maps by dilated convolutions with adaptive receptive fields, which are adjusted according to deep feature maps. In addition, a novel mutual loss is proposed to utilize the dependency between the foreground and background networks, thereby enabling the reciprocally influence within these two networks. Consequently, this mutual training strategy enables the semi-supervised learning and improve the boundary-sensitivity. Training with Skin Imaging Collaboration (ISIC) 2018 skin lesion segmentation dataset, our method achieves a dice co-efficient of 86.4% and shows better performance compared with state-of-the-art melanoma segmentation methods.
