论文标题
MMMNA-NET用于脑肿瘤患者的总生存时间预测
MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients
论文作者
论文摘要
总生存时间（OS）时间是胶质瘤情况最重要的评估指数之一。多模式磁共振成像（MRI）扫描在神经胶质瘤预后OS时间的研究中起重要作用。为多模式MRI问题的OS时间预测提出了几种基于深度学习的方法。但是，这些方法通常在深度学习网络的开头或结束时融合多模式信息，并且缺乏来自不同尺度的特征的融合。此外，网络末尾的融合始终将全球适应全球（例如，在全球平均池输出串联后完全连接）或与本地局部（例如，双线性池）的局部连接，从而失去了与全球局部的局部信息。在本文中，我们提出了一种用于脑肿瘤患者多模式OS时间预测的新方法，该方法包含在不同尺度上引入的改进的非局部特征融合模块。我们的方法比当前最新方法获得了相对8.76％的改善（0.6989 vs. 0.6426的精度）。广泛的测试表明，我们的方法可以适应缺失方式的情况。该代码可从https://github.com/tangwen920812/mmmna-net获得。
Overall survival (OS) time is one of the most important evaluation indices for gliomas situations. Multimodal Magnetic Resonance Imaging (MRI) scans play an important role in the study of glioma prognosis OS time. Several deep learning-based methods are proposed for the OS time prediction on multi-modal MRI problems. However, these methods usually fuse multi-modal information at the beginning or at the end of the deep learning networks and lack the fusion of features from different scales. In addition, the fusion at the end of networks always adapts global with global (eg. fully connected after concatenation of global average pooling output) or local with local (eg. bilinear pooling), which loses the information of local with global. In this paper, we propose a novel method for multi-modal OS time prediction of brain tumor patients, which contains an improved nonlocal features fusion module introduced on different scales. Our method obtains a relative 8.76% improvement over the current state-of-art method (0.6989 vs. 0.6426 on accuracy). Extensive testing demonstrates that our method could adapt to situations with missing modalities. The code is available at https://github.com/TangWen920812/mmmna-net.
