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.

本文提出了一种全注意力 U-Net 模型，通过使用通道注意力和空间通道注意力在 U-Net 编码器和解码器的不同侧面，实现了对头部 CT 扫描中颅内出血类型的准确形状提取和分类，模拟结果显示相比基线（ResNet50 + U-Net），改进了 31.8％，并在注意力有限的情况下取得了更好的性能。

全关注 U-NET 用于头部 CT 图像中颅内出血的语义分割

All Attention U-NET for Semantic Segmentation of Intracranial  Hemorrhages In Head CT Images

In clinical settings, intracranial hemorrhages (ICH) are routinely diagnosed
using non-contrast CT (NCCT) for severity assessment. Accurate automated
segmentation of ICH lesions is the initial and essential step, immensely useful
for such assessment. However, compared to other structural imaging modalities
such as MRI, in NCCT images ICH appears with very low contrast and poor SNR.
Over recent years, deep learning (DL)-based methods have shown great potential,
however, training them requires a huge amount of manually annotated
lesion-level labels, with sufficient diversity to capture the characteristics
of ICH. In this work, we propose a novel weakly supervised DL method for ICH
segmentation on NCCT scans, using image-level binary classification labels,
which are less time-consuming and labor-efficient when compared to the manual
labeling of individual ICH lesions. Our method initially determines the
approximate location of ICH using class activation maps from a classification
network, which is trained to learn dependencies across contiguous slices. We
further refine the ICH segmentation using pseudo-ICH masks obtained in an
unsupervised manner. The method is flexible and uses a computationally light
architecture during testing. On evaluating our method on the validation data of
the MICCAI 2022 INSTANCE challenge, our method achieves a Dice value of 0.55,
comparable with those of existing weakly supervised method (Dice value of
0.47), despite training on a much smaller training data.

通过使用图像级别的二元分类标签，我们提出了一种新颖的 NCCT 扫描弱监督深度学习方法用于 ICH 分割，该方法通过分类网络的类激活图确定 ICH 的大致位置，并使用无监督方法获取的伪 ICH 掩膜进一步改进 ICH 分割