论文标题
沿正交水平相交的体素注意U-NET，用于头部CT的脑内出血分段
Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT
论文作者
论文摘要
我们提出了一种新颖且灵活的基于注意力的U-NET结构，称为“沿正交级别的素脉冲关注U-NET”（VILA-UNET），用于颅内出血（ICH）分割任务，在2022年对非构造计算机造影（CT）的2022数据挑战中。通过有效地通过我们提议的Viola注意力插入U-NET解码分支，通过有效地融合了融合的空间正交和跨通道特征，从而提高了ICH分割的性能。在5倍的交叉验证和在线验证期间，中提琴 - Unet的表现优于强大的基线NNU-NET模型。我们的解决方案是所有四个性能指标（即DSC，HD，NSD和RVD）的挑战验证阶段的获胜者。 \ url {https://github.com/samleoqh/viola-unet}公开获得Viola-Unet AI工具的代码基础，预估计的权重和Docker图像。
We propose a novel and flexible attention based U-Net architecture referred to as "Voxels-Intersecting Along Orthogonal Levels Attention U-Net" (viola-Unet), for intracranial hemorrhage (ICH) segmentation task in the INSTANCE 2022 Data Challenge on non-contrast computed tomography (CT). The performance of ICH segmentation was improved by efficiently incorporating fused spatially orthogonal and cross-channel features via our proposed Viola attention plugged into the U-Net decoding branches. The viola-Unet outperformed the strong baseline nnU-Net models during both 5-fold cross validation and online validation. Our solution was the winner of the challenge validation phase in terms of all four performance metrics (i.e., DSC, HD, NSD, and RVD). The code base, pretrained weights, and docker image of the viola-Unet AI tool are publicly available at \url{https://github.com/samleoqh/Viola-Unet}.
