Dynamic Image for 3D MRI Image Alzheimer's Disease Classification
Autor: | Xing, Xin, Liang, Gongbo, Blanton, Hunter, Rafique, Muhammad Usman, Wang, Chris, Lin, Ai-Ling, Jacobs, Nathan |
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Rok vydání: | 2020 |
Předmět: | |
Druh dokumentu: | Working Paper |
DOI: | 10.1007/978-3-030-66415-2_23 |
Popis: | 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. Comment: Accepted to ECCV2020 Workshop on BioImage Computing |
Databáze: | arXiv |
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