论文标题
RSNA颅内出血检测竞赛的有效基于变压器的解决方案
An Effective Transformer-based Solution for RSNA Intracranial Hemorrhage Detection Competition
论文作者
论文摘要
我们提出了一种有效的颅内出血检测方法（IHD），该方法超过了RSNA-IHD竞争中获胜者解决方案的性能（2019年）。同时，与获胜者的解决方案相比，我们的模型仅采用四分之一的参数和10％的失败。 IHD任务需要预测输入脑CT的每个切片的出血类别。我们回顾了北美放射学会（RSNA）在2019年举行的IHD竞争的前5个解决方案。几乎所有顶级解决方案都依赖于2D卷积网络和顺序模型（双向GRU或LSTM）分别提取斜线内和跨板板的特征。所有顶部解决方案都通过利用模型集合来增强性能，并且模型编号从7到31不等。在过去的几年中，由于计算机视觉制度（尤其是基于变压器）的模型已经取得了很大进展，因此我们介绍了基于变压器的技术来提取INSLICE内部和IHD任务的跨度式视图中的功能。此外，将半监督的方法嵌入我们的工作流程中，以进一步提高性能。该代码在手稿中可用。
We present an effective method for Intracranial Hemorrhage Detection (IHD) which exceeds the performance of the winner solution in RSNA-IHD competition (2019). Meanwhile, our model only takes quarter parameters and ten percent FLOPs compared to the winner's solution. The IHD task needs to predict the hemorrhage category of each slice for the input brain CT. We review the top-5 solutions for the IHD competition held by the Radiological Society of North America(RSNA) in 2019. Nearly all the top solutions rely on 2D convolutional networks and sequential models (Bidirectional GRU or LSTM) to extract intra-slice and inter-slice features, respectively. All the top solutions enhance the performance by leveraging the model ensemble, and the model number varies from 7 to 31. In the past years, since much progress has been made in the computer vision regime especially Transformer-based models, we introduce the Transformer-based techniques to extract the features in both intra-slice and inter-slice views for IHD tasks. Additionally, a semi-supervised method is embedded into our workflow to further improve the performance. The code is available in the manuscript.
