论文标题
通过深神经网络识别心脏MRI取向，并提高预测准确性的方法
Recognition of Cardiac MRI Orientation via Deep Neural Networks and a Method to Improve Prediction Accuracy
论文作者
论文摘要
在大多数医学图像处理任务中，图像的方向会影响计算结果。但是，手动重新定向图像浪费时间和精力。在本文中，我们研究了识别心脏MRI取向并使用深层神经网络解决此问题的问题。对于MRI的多种序列和模态，我们提出了一种转移学习策略，该策略将我们提出的模型从单个模态调整到多种方式。我们还提出了一种使用投票的预测方法。结果表明，深度神经网络是识别心脏MRI取向的有效方法，投票预测方法可以提高准确性。
In most medical image processing tasks, the orientation of an image would affect computing result. However, manually reorienting images wastes time and effort. In this paper, we study the problem of recognizing orientation in cardiac MRI and using deep neural network to solve this problem. For multiple sequences and modalities of MRI, we propose a transfer learning strategy, which adapts our proposed model from a single modality to multiple modalities. We also propose a prediction method that uses voting. The results shows that deep neural network is an effective way in recognition of cardiac MRI orientation and the voting prediction method could improve accuracy.
