论文标题
DCSAU网络：用于医学图像分割的更深，更紧凑的分裂注意U-NET
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation
论文作者
论文摘要
卷积神经网络（CNN）的深度学习体系结构在计算机视野领域取得了杰出的成功。 CNN构建的编码器架构U-Net在生物医学图像分割方面取得了巨大突破，并且已在各种实用的情况下应用。但是，编码器部分中每个下采样层和简单堆积的卷积的平等设计不允许U-NET从不同深度提取足够的特征信息。医学图像的复杂性日益增加为现有方法带来了新的挑战。在本文中，我们提出了一个更深层，更紧凑的分裂注意U形网络（DCSAU-NET），该网络有效地利用了基于两个新型框架的低级和高级语义信息：主要特征保护和紧凑的分裂专门块。我们评估了CVC-ClinicDB，2018年数据科学碗，ISIC-2018和SEGPC-2021数据集的建议模型。结果，DCSAU-NET显示出比其他最新方法（SOTA）方法更好的性能（SOTA）方法，从联合（MIOU）和F1-SOCRE的平均值来看。更重要的是，提出的模型在具有挑战性的图像上表现出了出色的分割性能。可以在https://github.com/xq141839/dcsau-net上找到我们工作的代码和更多技术细节。
Deep learning architecture with convolutional neural network (CNN) achieves outstanding success in the field of computer vision. Where U-Net, an encoder-decoder architecture structured by CNN, makes a great breakthrough in biomedical image segmentation and has been applied in a wide range of practical scenarios. However, the equal design of every downsampling layer in the encoder part and simply stacked convolutions do not allow U-Net to extract sufficient information of features from different depths. The increasing complexity of medical images brings new challenges to the existing methods. In this paper, we propose a deeper and more compact split-attention u-shape network (DCSAU-Net), which efficiently utilises low-level and high-level semantic information based on two novel frameworks: primary feature conservation and compact split-attention block. We evaluate the proposed model on CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018 and SegPC-2021 datasets. As a result, DCSAU-Net displays better performance than other state-of-the-art (SOTA) methods in terms of the mean Intersection over Union (mIoU) and F1-socre. More significantly, the proposed model demonstrates excellent segmentation performance on challenging images. The code for our work and more technical details can be found at https://github.com/xq141839/DCSAU-Net.
