International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12

line decor
  
line decor

 Volume 5, Issue 8 (August 2018), Pages: 95-103

----------------------------------------------

 Original Research Paper

 Title: Feature map size selection for fMRI classification on end-to-end deep convolutional neural networks

 Author(s): Farahana Suhaimi *, Zaw Zaw Htike

 Affiliation(s):

 Faculty of Engineering, International Islamic University Malaysia, Gombak, Malaysia

 https://doi.org/10.21833/ijaas.2018.08.012

 Full Text - PDF          XML

 Abstract:

The emergence of convolutional neural networks (CNN) in various fields has also paved numerous ways for advancement in the field of medical imaging. This paper focuses on functional magnetic resonance imaging (fMRI) in the field of neuroimaging. It has high temporal resolution and robust to control or non-control subjects. CNN analysis on structural magnetic resonance imaging (MRI) and fMRI datasets is compared to rule out one of the grey areas in building CNNs for medical imaging analysis. This study focuses on the feature map size selection on fMRI datasets with CNNs where the selected sizes are evaluated for their performances. Although few outstanding studies on fMRI have been published, the availability of diverse previous studies on MRI previous works impulses us to study to learn the pattern of feature map sizes for CNN configuration. Six configurations are analyzed with prominent public fMRI dataset, names as Human Connectome Project (HCP). This dataset is widely used for any type of fMRI classification. With three set of data divisions, the accuracy values for validation set of fMRI classification are assessed and discussed. Despite the fact that only one slice of every 118 subjects' temporal brain images is used in the study, the validation of classification for three training-excluded subjects known as validation set, has proven the need for feature map size selection. This paper emphasizes the indispensable step of selecting the feature map sizes when designing CNN for fMRI classification. In addition, we provide proofs that validation set should consist of distinct subjects for definite evaluation of any model performance. 

 © 2018 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Classification, Functional MRI, Deep learning, CNN, Feature map

 Article History: Received 1 March 2018, Received in revised form 29 May 2018, Accepted 4 June 2018

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.08.012

 Citation:

 Suhaimi F and Htike ZZ (2018). Feature map size selection for fMRI classification on end-to-end deep convolutional neural networks. International Journal of Advanced and Applied Sciences, 5(8): 95-103

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I8/Suhaimi.html

----------------------------------------------

 References (34) 

  1. Bengio Y (2013). Deep learning of representations: Looking forward. In International Conference on Statistical Language and Speech Processing, Springer, Berlin, Heidelberg, Germany: 1-37. https://doi.org/10.1007/978-3-642-39593-2_1   [Google Scholar] 
  2. Burgh HK, Schmidt R, Westeneng HJ, Reus MA, Berg LH, and Heuvel MP (2017). Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis. NeuroImage: Clinical, 13: 361-369. https://doi.org/10.1016/j.nicl.2016.10.008   [Google Scholar]  PMid:28070484 PMCid:PMC5219634 
  3. Cohen JD, Daw N, Engelhardt B, Hasson U, Li K, Niv Y (2017). Computational approaches to fMRI analysis. Nature Neuroscience, 20(3): 304-313. https://doi.org/10.1038/nn.4499   [Google Scholar]  PMid:28230848 PMCid:PMC5457304     
  4. Cui Z, Yang J, and Qiao Y (2016). Brain MRI segmentation with patch-based CNN approach. In the 35th Chinese Control Conference, IEEE, Chengdu, China: 7026-7031. https://doi.org/10.1109/ChiCC.2016.7554465   [Google Scholar] 
  5. Eklund A, Nichols TE, and Knutsson H (2016). Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences, 113(28): 7900-7905. https://doi.org/10.1073/pnas.1602413113   [Google Scholar]  PMid:27357684 PMCid:PMC4948312     
  6. Gehring J, Auli M, Grangier D, Yarats D, and Dauphin YN (2017). Convolutional sequence to sequence learning. Available online at: https://arxiv.org/abs/1705.03122   [Google Scholar]     
  7. Gollapudi S (2016). Practical machine learning. Packt Publishing, Birmingham, UK.   [Google Scholar]     
  8. Greenspan H, Ginneken B, and Summers RM (2016). Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging, 35(5): 1153-1159. https://doi.org/10.1109/TMI.2016.2553401   [Google Scholar] 
  9. Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, and Lew MS (2016). Deep learning for visual understanding: A review. Neurocomputing, 187: 27-48. https://doi.org/10.1016/j.neucom.2015.09.116   [Google Scholar] 
  10. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, and Larochelle H (2017). Brain tumor segmentation with deep neural networks. Medical Image Analysis, 35: 18-31. https://doi.org/10.1016/j.media.2016.05.004   [Google Scholar]  PMid:27310171 
  11. Huang H, Hu X, Zhao Y, Makkie M, Dong Q, Zhao S, and Liu T (2017). Modeling Task fMRI Data via Deep Convolutional Autoencoder. IEEE Transactions on Medical Imaging. https://doi.org/10.1007/978-3-319-59050-9_33   [Google Scholar] 
  12. Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Glocker B (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36: 61-78. https://doi.org/10.1016/j.media.2016.10.004   [Google Scholar]  PMid:27865153 
  13. Karpathy A and Fei-Fei L (2015). Deep visual-semantic alignments for generating image descriptions. In the IEEE Conference on Computer Vision and Pattern Recognition: 3128-3137. https://doi.org/10.1109/CVPR.2015.7298932   [Google Scholar] 
  14. Krizhevsky A, Sutskever I, and Hinton GE (2012). Imagenet classification with deep convolutional neural networks. In: Touretzky DS, Mozer MC, and Hasselmo ME (Eds.), Advances in neural information processing systems: 1097-1105. MIT Press, Cambridge, Massachusetts, USA.[Google Scholar]     
  15. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, and Turner R (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences, 89(12): 5675-5679. https://doi.org/10.1073/pnas.89.12.5675   [Google Scholar]  PMid:1608978 
  16. LeCun Y, Bengio Y, and Hinton G (2015). Deep learning. Nature, 521(7553): 436-444. https://doi.org/10.1038/nature14539   [Google Scholar]  PMid:26017442 
  17. LeCun Y, Bottou L, Bengio Y, and Haffner P (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278-2324. https://doi.org/10.1109/5.726791   [Google Scholar] 
  18. Li R, Zhang W, Suk HI, Wang L, Li J, Shen D, and Ji S (2014). Deep learning based imaging data completion for improved brain disease diagnosis. In the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, Quebec City, QC, Canada: 305-312. https://doi.org/10.1007/978-3-319-10443-0_39   [Google Scholar] 
  19. Makkie M, Huang H, Zhao Y, Vasilakos AV, and Liu T (2017). Fast and scalable distributed deep convolutional autoencoder for FMRI big data analytics. Available online at: https://arxiv.org/abs/1710.08961   [Google Scholar]     
  20. Margeta J, Criminisi A, Cabrera Lozoya R, Lee DC, and Ayache N (2017). Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 5(5): 339-349. https://doi.org/10.1080/21681163.2015.1061448   [Google Scholar] 
  21. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, and Vanderplas J (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct): 2825-2830.   [Google Scholar]     
  22. Pereira S, Pinto A, Alves V, and Silva CA (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5): 1240-1251. https://doi.org/10.1109/TMI.2016.2538465   [Google Scholar]  PMid:26960222 
  23. Poldrack RA, Mumford JA, and Nichols TE (2011). Handbook of functional MRI data analysis. Cambridge University Press, New York, USA. https://doi.org/10.1017/CBO9780511895029   [Google Scholar] 
  24. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K (2017). CheXNet: Radiologist-Level pneumonia detection on chest X-rays with deep learning. Available online at: https://arxiv.org/abs/1711.05225   [Google Scholar]     
  25. Raschka S (2015). Python machine learning. Packt Publishing, Birmingham, UK.   [Google Scholar] PMid:26168742     
  26. Sarraf S and Tofighi G (2016). DeepAD: Alzheimer′ s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI. BioRxiv: 070441. https://doi.org/10.1101/070441   [Google Scholar] 
  27. Simonyan K and Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. Available online at: https://arxiv.org/abs/1409.1556   [Google Scholar]     
  28. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, and Rabinovich A (2015). Going deeper with convolutions. Available online at: https://arxiv.org/abs/1409.4842   [Google Scholar]     
  29. Valente G, Castellanos AL, Vanacore G, and Formisano E (2014). Multivariate linear regression of high‐dimensional fMRI data with multiple target variables. Human Brain Mapping, 35(5): 2163-2177. https://doi.org/10.1002/hbm.22318   [Google Scholar]  PMid:23881872 
  30. Valverde S, Cabezas M, Roura E, González-Villà S, Pareto D, Vilanova J C, and Lladó X (2017). Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage, 155, 159-168. https://doi.org/10.1016/j.neuroimage.2017.04.034   [Google Scholar]  PMid:28435096 
  31. Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TEJ, Bucholz R, and Della Penna S (2012). The Human Connectome Project: a data acquisition perspective. Neuroimage, 62(4): 2222-2231. https://doi.org/10.1016/j.neuroimage.2012.02.018   [Google Scholar]  PMid:22366334 PMCid:PMC3606888     
  32. Zafar R, Kamel N, Naufal M, Malik AS, Dass SC, Ahmad RF, and Reza F (2017). Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network. Journal of Integrative Neuroscience, 16(3): 275-289. https://doi.org/10.3233/JIN-170016   [Google Scholar]  PMid:28891512 
  33. Zhao L and Jia K (2016). Multiscale CNNs for brain tumor segmentation and diagnosis. Computational and mathematical methods in medicine, 2016(7): 1–7. https://doi.org/10.1155/2016/8356294   [Google Scholar]  PMid:27069501 PMCid:PMC4812495 
  34. Zhao Y, Dong Q, Zhang S, Zhang W, Chen H, Jiang X, and Liu T (2017). Automatic recognition of FMRI-derived functional networks using 3D convolutional neural networks. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2017.2715281   [Google Scholar]