DoubleU-NetPlus：一种用于医学图像语义分割的新型注意力与上下文引导双U-Net多尺度残差特征融合网络
DoubleU-NetPlus: A Novel Attention and Context Guided Dual U-Net with Multi-Scale Residual Feature Fusion Network for Semantic Segmentation of Medical Images
AI总结：
针对医学图像多尺度、边界模糊等分割难题，提出双U-Net架构DoubleU-NetPlus，融合EfficientNetB7、多核残差卷积与多重注意力模块以增强多尺度上下文特征建模。
摘要：
在医学图像中准确分割感兴趣区域，可为制定危及生命疾病的有效治疗方案提供重要途径。对于U-Net及其最先进的变体（如CE-Net和DoubleU-Net）而言，有效建模网络卷积单元的更高级输出特征图仍然具有挑战性，这主要是由于医学图像中感兴趣区域存在多种尺度、上下文环境复杂、边界模糊以及纹理形态多样。本文中，我们利用多上下文特征和多种注意力策略来增强网络对判别性特征表示的建模能力，从而实现更准确的医学图像分割，并提出了一种名为DoubleU-NetPlus的新型基于双U-Net的架构。DoubleU-NetPlus包含若干架构改进。具体而言，我们集成EfficientNetB7作为特征编码器模块、新设计的多核残差卷积模块，以及基于自适应特征重新校准注意力的空洞空间金字塔池化模块，以逐步且精确地积累具有判别性的多尺度高级上下文特征图并突出显著区域。此外，我们引入了新型三重注意力门模块和混合三重注意力模块，以促进对相关医学图像特征的选择性建模。此外，为缓解梯度消失问题并将高分辨率特征与更深层空间细节相结合，标准卷积操作被替换为注意力引导的残差卷积操作，……
英文摘要：
Accurate segmentation of the region of interest in medical images can provide an essential pathway for devising effective treatment plans for life-threatening diseases. It is still challenging for U-Net, and its state-of-the-art variants, such as CE-Net and DoubleU-Net, to effectively model the higher-level output feature maps of the convolutional units of the network mostly due to the presence of various scales of the region of interest, intricacy of context environments, ambiguous boundaries, and multiformity of textures in medical images. In this paper, we exploit multi-contextual features and several attention strategies to increase networks' ability to model discriminative feature representation for more accurate medical image segmentation, and we present a novel dual U-Net-based architecture named DoubleU-NetPlus. The DoubleU-NetPlus incorporates several architectural modifications. In particular, we integrate EfficientNetB7 as the feature encoder module, a newly designed multi-kernel residual convolution module, and an adaptive feature re-calibrating attention-based atrous spatial pyramid pooling module to progressively and precisely accumulate discriminative multi-scale high-level contextual feature maps and emphasize the salient regions. In addition, we introduce a novel triple attention gate module and a hybrid triple attention module to encourage selective modeling of relevant medical image features. Moreover, to mitigate the gradient vanishing issue and incorporate high-resolution features with deeper spatial details, the standard convolution operation is replaced with the attention-guided residual convolution operations, ...
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