Abstract
Alzheimer’s disease (AD) leads to irreversible cognitive decline, with Mild Cognitive Impairment (MCI) as its prodromal stage. Early detection of AD and related dementia is crucial for timely treatment and slowing disease progression. However, classifying cognitive normal (CN), MCI, and AD subjects using machine learning models faces class imbalance, necessitating the use of balanced accuracy as a suitable metric. To enhance model performance and balanced accuracy, we introduce a novel method called VS-Opt-Net. This approach incorporates the recently developed vector-scaling (VS) loss into a machine learning pipeline named STREAMLINE. Moreover, it employs Bayesian optimization for hyperparameter learning of both the model and loss function. VS-Opt-Net not only amplifies the contribution of minority examples in proportion to the imbalance level but also addresses the challenge of generalization in training deep networks. In our empirical study, we use MRI-based brain regional measurements as features to conduct the CN vs MCI and AD vs MCI binary classifications. We compare the balanced accuracy of our model with other machine learning models and deep neural network loss functions that also employ class-balanced strategies. Our findings demonstrate that after hyperparameter optimization, the deep neural network using the VS loss function substantially improves balanced accuracy. It also surpasses other models in performance on the AD dataset. Moreover, our feature importance analysis highlights VS-Opt-Net’s ability to elucidate biomarker differences across dementia stages.
This work was supported in part by the NIH grants U01 AG066833, R01 LM013463, U01 AG068057, P30 AG073105, and R01 AG071470, and the NSF grant IIS 1837964. Data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative database (adni.loni.usc.edu), which was funded by NIH U01 AG024904.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For existing machine learning models, optimized parameters can be found in https://github.com/UrbsLab/STREAMLINE.
- 2.
For the latest information, visit www.adni-info.org.
References
Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2623–2631 (2019)
Association, A., et al.: 2012 Alzheimer’s disease facts and figures. Alzheimer’s & Dement. 8(2), 131–168 (2012)
Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
De Santi, S., et al.: Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiol. Aging 22(4), 529–539 (2001)
Du, A.T.: Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 71(4), 441–447 (2001)
Dubey, R., Zhou, J., Wang, Y., Thompson, P.M., Ye, J.: Analysis of sampling techniques for imbalanced data: an n = 648 ADNI study. Neuroimage 87, 220–241 (2014)
Fan, Y., Batmanghelich, N., Clark, C.M., Davatzikos, C., Initiative, A.D.N., et al.: Spatial patterns of brain atrophy in mci patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39(4), 1731–1743 (2008)
Hu, S., Yu, W., Chen, Z., Wang, S.: Medical image reconstruction using generative adversarial network for Alzheimer disease assessment with class-imbalance problem. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp. 1323–1327. IEEE (2020)
Kim, D., et al.: A graph-based integration of multimodal brain imaging data for the detection of early mild cognitive impairment (E-MCI). In: Shen, L., Liu, T., Yap, P.-T., Huang, H., Shen, D., Westin, C.-F. (eds.) MBIA 2013. LNCS, vol. 8159, pp. 159–169. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02126-3_16
Kini, G.R., Paraskevas, O., Oymak, S., Thrampoulidis, C.: Label-imbalanced and group-sensitive classification under overparameterization. In: Advances in Neural Information Processing Systems, vol. 34, pp. 18970–18983 (2021)
Li, J., et al.: Persistent feature analysis of multimodal brain networks using generalized fused lasso for EMCI identification. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 44–52. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_5
Li, M., Zhang, X., Thrampoulidis, C., Chen, J., Oymak, S.: Autobalance: Optimized loss functions for imbalanced data. In: Advances in Neural Information Processing Systems, vol. 34, pp. 3163–3177 (2021)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Menon, A.K., Jayasumana, S., Rawat, A.S., Jain, H., Veit, A., Kumar, S.: Long-tail learning via logit adjustment. arXiv preprint arXiv:2007.07314 (2020)
Miller, M.I., et al.: Amygdala atrophy in MCI/Alzheimer’s disease in the BIOCARD cohort based on diffeomorphic morphometry. In: Medical Image Computing and Computer-Assisted Intervention: MICCAI... International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 2012, p. 155. NIH Public Access (2012)
Miller, M.I., et al.: Amygdala atrophy in MCI/Alzheimer’s disease in the BIOCARD cohort based on diffeomorphic morphometry. In: Medical Image Computing and Computer-Assisted Intervention: MICCAI. International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 2012, p. 155. NIH Public Access (2012)
Puspaningrum, E.Y., Wahid, R.R., Amaliyah, R.P., et al.: Alzheimer’s disease stage classification using deep convolutional neural networks on oversampled imbalance data. In: 2020 6th Information Technology International Seminar (ITIS), pp. 57–62. IEEE (2020)
Rasmussen, J., Langerman, H.: Alzheimer’s disease – why we need early diagnosis. Degenerative Neurol. Neuromuscul. Dis. Volume 9, 123–130 (2019)
Sadegh-Zadeh, S.A., et al.: An approach toward artificial intelligence Alzheimer’s disease diagnosis using brain signals. Diagn. 13(3), 477 (2023)
Shen, L., et al.: Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. In: Liu, T., Shen, D., Ibanez, L., Tao, X. (eds.) MBIA 2011. LNCS, vol. 7012, pp. 27–34. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24446-9_4
Tarzanagh, D.A., Hou, B., Tong, B., Long, Q., Shen, L.: Fairness-aware class imbalanced learning on multiple subgroups. In: Uncertainty in Artificial Intelligence, pp. 2123–2133. PMLR (2023)
Tong, B., et al.: Comparing amyloid imaging normalization strategies for Alzheimer’s disease classification using an automated machine learning pipeline. AMIA Jt. Summits Transl. Sci. Proc. 2023, 525–533 (2023)
Urbanowicz, R., Zhang, R., Cui, Y., Suri, P.: Streamline: a simple, transparent, end-to-end automated machine learning pipeline facilitating data analysis and algorithm comparison. In: Genetic Programming Theory and Practice XIX, pp. 201–231. Springer (2023). https://doi.org/10.1007/978-981-19-8460-0_9
Uwishema, O., et al.: Is Alzheimer’s disease an infectious neurological disease? a review of the literature. Brain Behav. 12(8), e2728 (2022)
Wang, X., et al.: Exploring automated machine learning for cognitive outcome prediction from multimodal brain imaging using streamline. AMIA Jt. Summits Transl. Sci. Proc. 2023, 544–553 (2023)
Weiner, M.W., Veitch, D.P., Aisen, P.S., et al.: The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement. 9(5), e111-94 (2013)
Weiner, M.W., Veitch, D.P., Aisen, P.S., et al.: Recent publications from the Alzheimer’s disease neuroimaging initiative: reviewing progress toward improved AD clinical trials. Alzheimer’s Dement. 13(4), e1–e85 (2017)
Ye, H.J., Chen, H.Y., Zhan, D.C., Chao, W.L.: Identifying and compensating for feature deviation in imbalanced deep learning. arXiv preprint arXiv:2001.01385 (2020)
Zeng, L., Li, H., Xiao, T., Shen, F., Zhong, Z.: Graph convolutional network with sample and feature weights for Alzheimer’s disease diagnosis. Inf. Process. Manage. 59(4), 102952 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tong, B. et al. (2024). Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham. https://doi.org/10.1007/978-3-031-45676-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-45676-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45675-6
Online ISBN: 978-3-031-45676-3
eBook Packages: Computer ScienceComputer Science (R0)
