论文标题
在3D CT扫描中，具有卷积和长期短期记忆神经网络中的准确有效的颅内出血检测和亚型分类
Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks
论文作者
论文摘要
在本文中，我们介绍了RSNA颅内出血检测挑战的系统。所提出的系统基于由卷积神经网络（CNN）组成的轻质深度神经网络体系结构，该卷积神经网络（CNN）将其作为输入单个CT切片以及长期的短期内存（LSTM）网络，该网络将作为CNN提供的输入特征嵌入式。为了进行有效的处理，我们考虑了各种特征选择方法，以生成LSTM有用的CNN特征的子集。此外，我们将CT切片减少了2倍，使自己可以更快地训练模型。即使我们的模型旨在平衡速度和准确性，我们还报告了最终测试集的加权平均日志损失为0.04989，这使我们从总共1345名参与者中排名前30名（2％）。尽管我们的计算基础架构不允许使用，但是以原始规模处理CT切片可能会提高性能。为了使其他人能够重现我们的结果，我们在https://github.com/warchildmd/ihd上提供代码作为开源。在挑战之后，我们进行了放射科医生的主观颅内出血检测评估，这表明我们的深层模型的表现与专门从事阅读CT扫描的医生的表现相当。我们工作的另一个贡献是将grad-CAM可视化整合到我们的系统中，为其预测提供有用的解释。因此，当需要快速诊断或对颅内出血检测的第二种意见或第二种意见时，我们将系统视为可行的选择。
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2x, allowing ourselves to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. Although our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source at https://github.com/warchildmd/ihd. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.
