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- W2529129926 abstract "Semantic segmentation of functional magnetic resonance imaging (fMRI) makes great sense for pathology diagnosis and decision system of medical robots. The multi-channel fMRI provides more information of the pathological features. But the increased amount of data causes complexity in feature detections. This paper proposes a principal component analysis (PCA)-aided fully convolutional network to particularly deal with multi-channel fMRI. We transfer the learned weights of contemporary classification networks to the segmentation task by fine-tuning. The results of the convolutional network are compared with various methods e.g. k-NN. A new labeling strategy is proposed to solve the semantic segmentation problem with unclear boundaries. Even with a small-sized training dataset, the test results demonstrate that our model outperforms other pathological feature detection methods. Besides, its forward inference only takes 90 milliseconds for a single set of fMRI data. To our knowledge, this is the first time to realize pixel-wise labeling of multi-channel magnetic resonance image using FCN." @default.
- W2529129926 created "2016-10-14" @default.
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- W2529129926 date "2016-10-06" @default.
- W2529129926 modified "2023-10-10" @default.
- W2529129926 title "PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI" @default.
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- W2529129926 doi "https://doi.org/10.48550/arxiv.1610.01732" @default.
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