论文标题
3D MRI图像阿尔茨海默氏病分类的动态图像
Dynamic Image for 3D MRI Image Alzheimer's Disease Classification
论文作者
论文摘要
我们建议将2D CNN体系结构应用于阿尔茨海默氏病分类的3D MRI图像。训练3D卷积神经网络（CNN）耗时且计算昂贵。我们利用近似秩池将3D MRI图像量转换为2D图像，以用作2D CNN的输入。我们显示，我们提出的CNN模型可实现$ 9.5 \％$ $比基线3D模型更好的阿尔茨海默氏病分类精度。我们还表明，我们的方法可以进行有效的培训，与3D CNN模型相比，仅需要20％的训练时间。该代码可在线提供：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.
