Intracranial hemorrhages in head CT scans serve as a first line tool to help specialists diagnose different types. However, their types have diverse shapes in the same type but similar confusing shape, size and location between types. To solve this problem, this paper proposes an all attention U-Net. It uses channel attentions in the U-Net encoder side to enhance class specific feature extraction, and space and channel attentions in the U-Net decoder side for more accurate shape extraction and type classification. The simulation results show up to a 31.8\% improvement compared to baseline, ResNet50 + U-Net, and better performance than in cases with limited attention.

翻译：头部CT扫描中的颅内出血是帮助专家诊断不同类型的第一线工具。然而，同一类型的颅内出血形状多样，而不同类型之间在形状、大小和位置上却存在相似且易混淆的特征。为解决这一问题，本文提出了一种全方位注意力U-Net。该网络在U-Net编码器侧使用通道注意力以增强类别特异性特征提取，并在解码器侧结合空间注意力与通道注意力，以实现更精确的形状提取和类型分类。仿真结果显示，与基线模型ResNet50 + U-Net相比，本方法性能提升高达31.8%，且优于注意力受限情况下的表现。