The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Deep Learning-Based Feature Representation for AD/MCI Classification
Open accessAbstract
In recent years, there has been a great interest in computer-aided diagnosis of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI). Unlike the previous methods that consider simple low-level features such as gray matter tissue volumes from MRI, mean signal intensities from PET, in this paper, we propose a deep learning-based feature representation with a stacked auto-encoder. We believe that there exist latent complicated patterns, e.g., non-linear relations, inherent in the low-level features. Combining latent information with the original low-level features helps build a robust model for AD/MCI classification with high diagnostic accuracy. Using the ADNI dataset, we conducted experiments showing that the proposed method is 95.9%, 85.0%, and 75.8% accurate for AD, MCI, and MCI-converter diagnosis, respectively.
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Cites
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Multimodal techniques for diagnosis and prognosis of Alzheimer's disease
Perrin, R. J., Fagan, A. M., & Holtzman, D. M. (2009) · Nature · doi:10.1038/nature08538 · Cited by 702
Cited by
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688
Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968
“We argue that this joint feature representation discriminates our method from the previous multimodal methods (Hinrichs et al., 2011; Suk and Shen, 2013; Zhang et al., 2011), which first extracted features from each modality independently, and then combined them through kernel machines.”
InfluentialSelf-citationApplications of deep learning and reinforcement learning to biological data
Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2790388 · Cited by 765
Multimodal deep learning models for early detection of Alzheimer’s disease stage
Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021) · Scientific Reports · doi:10.1038/s41598-020-74399-w · Cited by 732
Metadata
- Authors
- Suk, H.-I., & Shen, D.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Volume
- 16
- Issue
- Pt 2
- Pages
- 583-590
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1948745668
- Cited by
- 439
- References
- 3
- FWCI
- 56.09
- Type
- conference paper
- Language
- en
- Open access
- green
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