Abstract
As a novel non-neural network style deep learning method, the deep forest can perform effective feature learning without relying on a large amount of training data, thus brings us some opportunities to accurately classify brain networks (BNs) on limited fMRI data. Currently, preliminary attempts to use deep forest to classify BNs are already emerging. However, these studies simply adopted the sliding windows to scan the inputted BNs and failed to consider the inherent sparsity of BNs, which makes them susceptible to those redundant edges in BNs with little weight. In this paper, we propose a deep forest framework with sparse topological feature extraction and hash mapping (DF-STFEHM) for BN classification. Specifically, we first design an extremely random forest guided by a weighted random walk (ERF-WRW) to extract sparse topological features from BNs, where the random walk strategy is used to capture their topological structures and the weighted strategy is used to reduce the influence of redundant edges with little weight. Then, we map these sparse topological features into a compact hashing space by a kernel hashing, which can better preserve topological similarities of brain networks in the hashing space. Finally, the obtained hash codes are fed into the casForest to perform deeper feature learning and classification. Experimental results on ABIDE I and ADHD-200 datasets show that the DF-STFEHM outperforms several state-of-the-art methods on classification performance and accurately identifies abnormal brain regions.
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References
Ahmed, N., et al.: Role-based graph embeddings. IEEE Trans. Knowl. Data Eng. (2020)
Ball, G., et al.: Machine-learning to characterise neonatal functional connectivity in the preterm brain. Neuroimage 124, 267–275 (2016)
Behrouzi, T., Hatzinakos, D.: Graph variational auto-encoder for deriving EEG-based graph embedding. Pattern Recogn. 121, 108202 (2022)
Di Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659–667 (2014)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)
Guerra-Carrillo, B., Mackey, A.P., Bunge, S.A.: Resting-state fmri: a window into human brain plasticity. Neuroscientist 20(5), 522–533 (2014)
He, J., Liu, W., Chang, S.F.: Scalable similarity search with optimized kernel hashing. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1129–1138 (2010)
Huang, Z.A., Zhu, Z., Yau, C.H., Tan, K.C.: Identifying autism spectrum disorder from resting-state fmri using deep belief network. IEEE Trans. Neural Networks Learn. Syst. 32(7), 2847–2861 (2020)
Ji, J., Xing, X., Yao, Y., Li, J., Zhang, X.: Convolutional kernels with an element-wise weighting mechanism for identifying abnormal brain connectivity patterns. Pattern Recogn. 109, 107570 (2021)
Jie, B., Liu, M., Zhang, D., Shen, D.: Sub-network kernels for measuring similarity of brain connectivity networks in disease diagnosis. IEEE Trans. Image Process. 27(5), 2340–2353 (2018)
Kawahara, J., et al.: Brainnetcnn: convolutional neural networks for brain networks; towards predicting neurodevelopment. Neuroimage 146, 1038–1049 (2017)
Li, J., Ji, J., Liang, Y., Zhang, X., Wang, Z.: Deep forest with cross-shaped window scanning mechanism to extract topological features. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 688–691. IEEE (2019)
Li, L., et al.: Te-hi-gcn: an ensemble of transfer hierarchical graph convolutional networks for disorder diagnosis. Neuroinformatics, pp. 1–23 (2021)
Lluis, Borràs-Ferrís, Úrsula, Pérez-Ramírez, David, Moratal: Link-level functional connectivity neuroalterations in autism spectrum disorder: a developmental resting-state fmri study. Diagnostics (Basel, Switzerland) (2019)
Marzullo, A., Kocevar, G., Stamile, C., Durand-Dubief, F., Terracina, G., Calimeri, F., Sappey-Marinier, D.: Classification of multiple sclerosis clinical profiles via graph convolutional neural networks. Front. Neurosci. 13, 594 (2019)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Milham, M.P., Fair, D., Mennes, M., Mostofsky, S.H., et al.: The adhd-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2012)
Nicholson, A.A., et al.: Classifying heterogeneous presentations of ptsd via the default mode, central executive, and salience networks with machine learning. NeuroImage Clinical 27, 102262 (2020)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Qian, L., Li, Y., Wang, Y., Wang, Y., Cheng, X., Li, C., Cui, X., Jiao, G., Ke, X.: Shared and distinct topologically structural connectivity patterns in autism spectrum disorder and attention-deficit/hyperactivity disorder. Frontiers in Neuroscience 15 (2021)
Shao, L., Zhang, D., Du, H., Fu, D.: Deep forest in adhd data classification. IEEE Access 7, 137913–137919 (2019)
Torralba, A., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. Neuroimage 15(1), 273–289 (2002)
Xu, M.: Understanding graph embedding methods and their applications. SIAM Rev. 63(4), 825–853 (2021)
Zeng, K., Kang, J., Ouyang, G., Li, J., Han, J., Wang, Y., Sokhadze, E.M., Casanova, M.F., Li, X.: Disrupted brain network in children with autism spectrum disorder. Sci. Rep. 7(1), 1–12 (2017)
Zhang, H., Li, R., Wen, X., Li, Q., Wu, X.: Altered time-frequency feature in default mode network of autism based on improved hilbert-huang transform. IEEE J. Biomed. Health Inform. 25(2), 485–492 (2020)
Zhang, L., Wang, X.H., Li, L.: Diagnosing autism spectrum disorder using brain entropy: a fast entropy method. Comput. Methods Programs Biomed. 190, 105240 (2020)
Zhou, Y., Wu, C., Tan, L.: Biased random walk with restart for link prediction with graph embedding method. Physica A 570, 125783 (2021)
Zhou, Z.H., Feng, J.: Deep forest: towards an alternative to deep neural networks. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 3553–3559 (2017)
Zhu, D., et al.: Classification of major depressive disorder via multi-site weighted LASSO model. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 159–167. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_19
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This work was supported in part by R &D Program of Beijing Municipal Education Commission (KZ202210005009).
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Li, J., Ji, J. (2022). Deep Forest with Sparse Topological Feature Extraction and Hash Mapping for Brain Network Classification. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_12
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