DS$^2$Net：用于医学图像分割的细节-语义深度监督网络
DS$^2$Net: Detail-Semantic Deep Supervision Network for Medical Image Segmentation
AI总结：
提出DS$^2$Net，通过细节和语义增强模块实现多视图深度监督，并引入基于不确定性的损失自适应分配监督强度，在六个医学图像基准上优于现有方法。
摘要：
深度监督网络在医学图像领域展现出显著成效。然而，现有工作仅孤立地监督粗粒度语义特征或细粒度细节特征，这忽视了这两类特征在医学图像分析中存在关键关系的事实。我们提出细节-语义深度监督网络（DS$^2$Net），以发挥互补特征监督在医学图像分割中的优势。DS$^2$Net通过细节增强模块（DEM）和语义增强模块（SEM）协调低级细节和高级语义特征监督。DEM和SEM分别利用低级和高级特征图创建细节和语义掩码以增强特征监督。这是从单视图深度监督向多视图深度监督的新颖转变。DS$^2$Net还配备了一种新颖的基于不确定性的监督损失，它根据特征的不确定性自适应地分配不同尺度内特征的监督强度，从而规避了以往工作中典型的次优启发式设计。通过对在结肠镜、超声和显微镜下采集的六个基准进行广泛实验，我们证明DS$^2$Net在医学图像分析中始终优于最先进的方法。
英文摘要：
Deep Supervision Networks exhibit significant efficacy for the medical imaging community. Nevertheless, existing work merely supervises either the coarse-grained semantic features or fine-grained detailed features in isolation, which compromises the fact that these two types of features hold vital relationships in medical image analysis. We advocate the powers of complementary feature supervision for medical image segmentation, by proposing a Detail-Semantic Deep Supervision Network (DS$^2$Net). DS$^2$Net navigates both low-level detailed and high-level semantic feature supervision through Detail Enhance Module (DEM) and Semantic Enhance Module (SEM). DEM and SEM respectively harness low-level and high-level feature maps to create detail and semantic masks for enhancing feature supervision. This is a novel shift from single-view deep supervision to multi-view deep supervision. DS$^2$Net is also equipped with a novel uncertainty-based supervision loss that adaptively assigns the supervision strength of features within distinct scales based on their uncertainty, thus circumventing the sub-optimal heuristic design that typifies previous works. Through extensive experiments on six benchmarks captured under either colonoscopy, ultrasound and microscope, we demonstrate that DS$^2$Net consistently outperforms state-of-the-art methods for medical image analysis.
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