The cornerstone of stroke care is expedient management that varies depending
on the time since stroke onset. Consequently, clinical decision making is
centered on accurate knowledge of timing and often requires a radiologist to
interpret Computed Tomography (CT) of the brain to confirm the occurrence and
age of an event. These tasks are particularly challenging due to the subtle
expression of acute ischemic lesions and the dynamic nature of their
appearance. Automation efforts have not yet applied deep learning to estimate
lesion age and treated these two tasks independently, so, have overlooked their
inherent complementary relationship. To leverage this, we propose a novel
end-to-end multi-task transformer-based network optimized for concurrent
segmentation and age estimation of cerebral ischemic lesions. By utilizing
gated positional self-attention and CT-specific data augmentation, the proposed
method can capture long-range spatial dependencies while maintaining its
ability to be trained from scratch under low-data regimes commonly found in
medical imaging. Furthermore, to better combine multiple predictions, we
incorporate uncertainty by utilizing quantile loss to facilitate estimating a
probability density function of lesion age. The effectiveness of our model is
then extensively evaluated on a clinical dataset consisting of 776 CT images
from two medical centers. Experimental results demonstrate that our method
obtains promising performance, with an area under the curve (AUC) of 0.933 for
classifying lesion ages <=4.5 hours compared to 0.858 using a conventional
approach, and outperforms task-specific state-of-the-art algorithms.

本文提出了一种基于 Transformer 网络的多任务模型，用于同时对脑缺血损伤进行分割和估计时间，该模型利用门控位置自注意力和 CT 专用数据增强来捕捉长距离的空间依赖性，综合评估表明该方法在分割效果、时间分类性能和向先前工作提供的比较方面都表现出色。