作者
Marcia Hon, Naimul Mefraz Khan
发表日期
2017/11/13
研讨会论文
2017 IEEE International conference on bioinformatics and biomedicine (BIBM)
页码范围
1166-1169
出版商
IEEE
简介
Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years. Recent success of deep learning in computer vision have progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets consisting of natural images, and the fully-connected layer is re-trained with only a small number of MRI images. We employ image entropy to select the most informative slices for training. Through experimentation on the OASIS MRI dataset, we show that with training size almost 10 times …
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M Hon, NM Khan - 2017 IEEE International conference on bioinformatics …, 2017