Abstract
Subjective cognitive decline (SCD) is a preclinical phase of Alzheimer’s disease (AD) which occurs before the deficits could be detected by cognitive tests. It is highly desired to predict the progress of SCD for possible intervention of AD-related cognitive decline. Many neuroimaging-based methods have been developed for AD diagnosis, but there are few studies devoted to automated progress prediction of SCD due to the limited number of SCD subjects. Even though some studies proposed to transfer models (trained on AD/MCI) to SCD analysis, the significant domain shift between their data distributions may degrade the prediction performance. To this end, this paper tackles the problem of learning a model from the source data for which can directly generalize to an unseen target domain for SCD prediction. We propose a cost-sensitive meta-learning scheme to simultaneously improve the model generalization and its sensitivity in MRI-based SCD detection. During training, the source domain is divided into virtual meta-train and meta-test sets to explicitly simulate the scenario for early-stage detection of AD. Considering the importance of sensitivity for progressive status detection, we further introduce cost-sensitive learning to enhance the meta-optimization process by encouraging the model to gain higher sensitivity for SCD detection with simulated domain shift. Experiments conducted on the large-scale ADNI dataset and a small-scale SCD dataset have demonstrated the effectiveness of the proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jessen, F., et al.: A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s Dementia 10(6), 844–852 (2014)
Rabin, L.A., Smart, C.M., Amariglio, R.E.: Subjective cognitive decline in preclinical Alzheimer’s disease. Annu. Rev. Clin. Psychol. 13, 369–396 (2017)
Amariglio, R.E., et al.: Subjective cognitive complaints and amyloid burden in cognitively normal older individuals. Neuropsychologia 50(12), 2880–2886 (2012)
Jessen, F., et al.: AD dementia risk in late MCI, in early MCI, and in subjective memory impairment. Alzheimer’s Dementia 10(1), 76–83 (2014)
Jack, C.R., Jr., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Resonan. Imaging Official J. Int. Soc. Magn. Resonan. Med. 27(4), 685–691 (2008)
Liu, M., Zhang, J., Adeli, E., Shen, D.: Landmark-based deep multi-instance learning for brain disease diagnosis. Med. Image Anal. 43, 157–168 (2018)
Wang, M., Zhang, D., Huang, J., Yap, P.T., Shen, D., Liu, M.: Identifying autism spectrum disorder with multi-site fMRI via low-rank domain adaptation. IEEE Trans. Med. Imaging 39(3), 644–655 (2019)
Yao, D., Calhoun, V.D., Fu, Z., Du, Y., Sui, J.: An ensemble learning system for a 4-way classification of Alzheimer’s disease and mild cognitive impairment. J. Neurosci. Methods 302, 75–81 (2018)
Liu, Y., et al.: Joint neuroimage synthesis and representation learning for conversion prediction of subjective cognitive decline. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 583–592. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_57
Cheng, N., et al.: Self-weighted multi-task learning for subjective cognitive decline diagnosis. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 104–113. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_11
Wachinger, C., Reuter, M.: Domain adaptation for Alzheimer’s disease diagnostics. NeuroImage 139, 470–479 (2016)
Cheng, B., Liu, M., Zhang, D., Munsell, B.C., Shen, D.: Domain transfer learning for MCI conversion prediction. IEEE Trans. Biomed. Eng. 62(7), 1805–1817 (2015)
Hosseini-Asl, E., Keynton, R., El-Baz, A.: Alzheimer’s disease diagnostics by adaptation of 3D convolutional network. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 126–130. IEEE (2016)
Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516–524. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_59
Orbes-Arteaga, M., et al.: Multi-domain adaptation in brain MRI through paired consistency and adversarial learning. In: Wang, Q., et al. (eds.) DART/MIL3ID -2019. LNCS, vol. 11795, pp. 54–62. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33391-1_7
Li, W., Zhao, Y., Chen, X., Xiao, Y., Qin, Y.: Detecting Alzheimer’s disease on small dataset: a knowledge transfer perspective. IEEE J. Biomed. Health Inform. 23(3), 1234–1242 (2018)
Guan, H., Liu, Y., Yang, E., Yap, P.T., Shen, D., Liu, M.: Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification. Med. Image Anal. 71, 102076 (2021)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135. PMLR (2017)
Li, D., Yang, Y., Song, Y.Z., Hospedales, T.: Learning to generalize: Meta-learning for domain generalization. In: Proceedings of the AAAI Conference on Artificial Intelligence (2018)
Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 3981–3989 (2016)
Ling, C.X., Sheng, V.S.: Cost-sensitive learning and the class imbalance problem. Encyclopedia Mach. Learn. 2008, 231–235 (2011)
Kuo, W., Häne, C., Yuh, E., Mukherjee, P., Malik, J.: Cost-sensitive active learning for intracranial hemorrhage detection. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 715–723. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_82
Galdran, A., Dolz, J., Chakor, H., Lombaert, H., Ben Ayed, I.: Cost-sensitive regularization for diabetic retinopathy grading from eye fundus images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 665–674. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_64
Korolev, S., Safiullin, A., Belyaev, M., Dodonova, Y.: Residual and plain convolutional neural networks for 3D brain MRI classification. In: ISBI, pp. 835–838 (2017)
Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: ICLR (2014)
Yue, L., et al.: Prediction of 7-year’s conversion from subjective cognitive decline to mild cognitive impairment. Hum. Brain Mapp. 42(1), 192–203 (2021)
Felpete, A., et al.: Predicting progression in subjective cognitive decline (SCD) using a machine learning (ML) approach: the role of the complaint’s severity. Alzheimer’s Dementia 16, e043492 (2020)
Acknowledgements
H. Guan and M. Liu were partly supported by NIH grant (No. AG041721).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Guan, H., Liu, Y., Xiao, S., Yue, L., Liu, M. (2021). Cost-Sensitive Meta-learning for Progress Prediction of Subjective Cognitive Decline with Brain Structural MRI. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-87240-3_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87239-7
Online ISBN: 978-3-030-87240-3
eBook Packages: Computer ScienceComputer Science (R0)