Dynamic Image for 3D MRI Image Alzheimer's Disease Classification.
Dynamic Image for 3D MRI Image Alzheimer's Disease Classification.
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DOI：
10.1007/978-3-030-66415-2_23
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发表时间：
2020-08
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我们建议将2D CNN架构应用于3D MRI图像阿尔茨海默病分类。训练3D卷积神经网络（CNN）是耗时且计算昂贵的。我们利用近似秩池将3D MRI图像体积转换为2D图像，以用作2D CNN的输入。我们表明，我们提出的CNN模型比基线3D模型的阿尔茨海默病分类准确率高9.5%。我们还表明，我们的方法可以进行有效的训练，与3D CNN模型相比，只需要20%的训练时间。该代码可在线获取：https://github.com/UkyVision/alzheimer-project。
We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer’s disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves 9.5% better Alzheimer’s disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20% of the training time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.