Dynamic Image for 3D MRI image Alzheimer’s Disease classificationDownload PDF

Jul 20, 2020 (edited Sep 08, 2020)ECCV 2020 Workshop BIC Blind SubmissionReaders: Everyone
  • TL;DR: Training 3D MRI image by 2D CNN model.
  • Reviews Visibility: The authors agree that reviews are made publicly visible, if the submission is accepted.
  • Abstract: 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 dynamic image technology 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/xxx/xxx.
  • Keywords: Dynamic image, 2D CNN, MRI image, Alzheimer’s Disease
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