Deep learning based imaging data completion for improved brain disease diagnosis.
Deep learning based imaging data completion for improved brain disease diagnosis.
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DOI：
10.1007/978-3-319-10443-0_39
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发表时间：
2014
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结合多模式脑数据进行疾病诊断通常会提高性能。使用多通道数据的一个挑战是，数据通常是不完整的；也就是说，对于某些受试者来说，可能缺少某些通道。在这项工作中，我们提出了一种基于深度学习的多模式成像数据估计框架。我们的方法采用卷积神经网络的形式，其中输入和输出是两个体积模态。该网络包含大量的可训练参数，这些参数反映了输入和输出模式之间的关系。当对具有所有通道的受试者进行训练时，网络可以在给定输入通道的情况下估计输出通道。我们在阿尔茨海默病神经成像计划(ADNI)数据库上对我们的方法进行了评估，其中输入和输出模式分别是MRI和PET图像。结果表明，我们的方法明显优于以往的方法。
Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. A challenge in using multi-modality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes the form of convolutional neural networks, where the input and output are two volumetric modalities. The network contains a large number of trainable parameters that capture the relationship between input and output modalities. When trained on subjects with all modalities, the network can estimate the output modality given the input modality. We evaluated our method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, where the input and output modalities are MRI and PET images, respectively. Results showed that our method significantly outperformed prior methods.