深度残留网络改善阿尔茨海默病诊断

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI:10.1145/3107411.3108224

Aly A. Valliani, Ameet Soni

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引用次数: 59

摘要

我们提出了一个框架，利用深度残差cnn在大型非生物医学图像数据集上进行预训练。这些预训练的网络学习跨域特征，提高图像的低级解释。我们在脑成像数据上评估了我们的模型，并表明预训练和深度残差网络的使用对于从脑mri中看到阿尔茨海默病诊断的巨大改善至关重要。

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Deep Residual Nets for Improved Alzheimer's Diagnosis

We propose a framework that leverages deep residual CNNs pretrained on large, non-biomedical image data sets. These pretrained networks learn cross-domain features that improve low-level interpretation of images. We evaluate our model on brain imaging data and show that pretraining and the use of deep residual networks are crucial to seeing large improvements in Alzheimer's Disease diagnosis from brain MRIs.

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Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

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