论文标题
引导注意力为视网膜分段的剩余U-NET
Channel Attention Residual U-Net for Retinal Vessel Segmentation
论文作者
论文摘要
视网膜血管分割是诊断许多早期眼睛有关疾病的至关重要步骤。在这项工作中，我们提出了一种新的深度学习模型，即将注意力为剩余的U-NET（CAR-UNET）准确地分段视网膜血管和非血管像素。在此模型中，我们引入了一种新颖的修改有效的通道注意力（MECA），以通过考虑特征图之间的相互依赖性来增强网络的歧视能力。一方面，我们将MECA应用于传统U形网络中的“跳过连接”，而不是简单地将合同路径的特征图复制到相应的膨胀路径上。另一方面，我们提出了一个通道注意的双重残留块（CADRB），该块将MECA整合到残留结构中，作为构建所提出的CAR-UNET的核心结构。结果表明，我们提议的CAR-UNET已在三个公开可用的视网膜数据集上达到了最先进的性能：驱动器，Chase DB1和Stare。
Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases. In this work, we propose a new deep learning model, namely Channel Attention Residual U-Net (CAR-UNet), to accurately segment retinal vascular and non-vascular pixels. In this model, we introduced a novel Modified Efficient Channel Attention (MECA) to enhance the discriminative ability of the network by considering the interdependence between feature maps. On the one hand, we apply MECA to the "skip connections" in the traditional U-shaped networks, instead of simply copying the feature maps of the contracting path to the corresponding expansive path. On the other hand, we propose a Channel Attention Double Residual Block (CADRB), which integrates MECA into a residual structure as a core structure to construct the proposed CAR-UNet. The results show that our proposed CAR-UNet has reached the state-of-the-art performance on three publicly available retinal vessel datasets: DRIVE, CHASE DB1 and STARE.
