Abstract
The automatic early diagnosis of prodromal stages of Alzheimer’s disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-modal data provides richer and complementary information. However, existing techniques only consider lower order relations between the data and single/multi-modal imaging data. In this work, we introduce a novel semi-supervised hypergraph learning framework for Alzheimer’s disease diagnosis. Our framework allows for higher-order relations among multi-modal imaging and non-imaging data whilst requiring a tiny labelled set. Firstly, we introduce a dual embedding strategy for constructing a robust hypergraph that preserves the data semantics. We achieve this by enforcing perturbation invariance at the image and graph levels using a contrastive based mechanism. Secondly, we present a dynamically adjusted hypergraph diffusion model, via a semi-explicit flow, to improve the predictive uncertainty. We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer’s disease diagnosis.
C.-B. Schönlieb—The Alzheimer’s Disease Neuroimaging Initiative.
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Notes
- 1.
Data used were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).
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Acknowledgements
AIAR acknowledges support from CMIH and CCIMI, University of Cambridge. CR acknowledges support from the CCIMI and the EPSRC grant EP/W524141/1 ref. 2602161. ZK acknowledges support from the BBSRC (H012508, BB/P021255/1), Wellcome Trust (205067/Z/16/Z, 221633/Z/20/Z) and Royal Society (INF/R2/202107). CBS acknowledges the Philip Leverhulme Prize, the EPSRC fellowship EP/V029428/1, EPSRC grants EP/T003553/1, EP/N014588/1, Wellcome Trust 215733/Z/19/Z and 221633/Z/20/Z, Horizon 2020 No. 777826 NoMADS and the CCIMI.
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Aviles-Rivero, A.I., Runkel, C., Papadakis, N., Kourtzi, Z., Schönlieb, CB. (2022). Multi-modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13433. Springer, Cham. https://doi.org/10.1007/978-3-031-16437-8_69
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