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Volumn 2, Issue , 2015, Pages 355-362

Predicting Alzheimer's disease a neuroimaging study with 3D convolutional neural networks

Author keywords

Alzheimer's disease; Autoencoders; Classification; Convolutional neural networks; Deep learning; MRI; Neural networks; Neuroimaging; Pattern recognition

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; DIAGNOSIS; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; NEURAL NETWORKS; NEURODEGENERATIVE DISEASES; NEUROIMAGING;

EID: 84938872693     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (92)

References (13)
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    • A general and unifying framework for feature construction, in image-based pattern classification
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    • Batmanghelich, N., Taskar, B., and Davatzikos, C. (2009). A general and unifying framework for feature construction, in image-based pattern classification. In Information Processing in Medical Imaging, pages 423-434. Springer.
    • (2009) Information Processing in Medical Imaging , pp. 423-434
    • Batmanghelich, N.1    Taskar, B.2    Davatzikos, C.3
  • 3
    • 84872577736 scopus 로고    scopus 로고
    • Practical recommendations for gradient-based training of deep architectures
    • Springer
    • Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. In Neural Networks: Tricks of the Trade, pages 437-478. Springer.
    • (2012) Neural Networks: Tricks of the Trade , pp. 437-478
    • Bengio, Y.1
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    • 77953183471 scopus 로고    scopus 로고
    • What is the best multi-stage architecture for object recognition?
    • 2009 IEEE 12th International Conference on IEEE
    • Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2009). What is the best multi-stage architecture for object recognition? In Computer Vision, 2009 IEEE 12th International Conference on, pages 2146-2153. IEEE.
    • (2009) Computer Vision , pp. 2146-2153
    • Jarrett, K.1    Kavukcuoglu, K.2    Ranzato, M.3    LeCun, Y.4
  • 10
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    • Early diagnosis of Alzheimer's disease with deep learning
    • 2014 IEEE 11th International Symposium on IEEE
    • Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., and Feng, D. (2014). Early diagnosis of Alzheimer's disease with deep learning. In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on, pages 1015-1018. IEEE.
    • (2014) Biomedical Imaging (ISBI) , pp. 1015-1018
    • Liu, S.1    Liu, S.2    Cai, W.3    Pujol, S.4    Kikinis, R.5    Feng, D.6
  • 12
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    • Multiple instance learning for classification of dementia in brain mri
    • Tong, T., Wolz, R., Ga, Q., Guerrero, R., Hajnal, J., Rueckert, D., and Alzheimer's Dis Neuroimaging Initi (2014). Multiple instance learning for classification of dementia in brain mri. Medical image analysis, 18 (5):808-818.
    • (2014) Medical Image Analysis , vol.18 , Issue.5 , pp. 808-818
    • Tong, T.1    Wolz, R.2    Ga, Q.3    Guerrero, R.4    Hajnal, J.5    Rueckert, D.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.