  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.
