Self-normalized Classification of Parkinson’s Disease DaTscan Images
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Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9669261/9669139/09669820.pdf?arnumber=9669820
Reference30 articles.
1. When does Parkinson's disease begin?
2. Computer-aided diagnosis of Parkinson’s disease based on [123I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines
3. Voxel-based logistic analysis of PPMI control and Parkinson's disease DaTscans
4. Periodic leg movements in patients with Parkinson's disease are associated with reduced striatal dopamine transporter binding
5. Linear intensity normalization of FP-CIT SPECT brain images using the α-stable distribution
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1. Unsupervised deep frequency-channel attention factorization to non-linear feature extraction: A case study of identification and functional connectivity interpretation of Parkinson’s disease;Expert Systems with Applications;2024-06
2. Bridging Imaging and Clinical Scores in Parkinson’s Progression via Multimodal Self-Supervised Deep Learning;International Journal of Neural Systems;2024-05-22
3. Machine learning within the Parkinson’s progression markers initiative: Review of the current state of affairs;Frontiers in Aging Neuroscience;2023-02-13
4. Detecting Parkinson’s Disease through Gait Measures Using Machine Learning;Diagnostics;2022-10-03
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