Cited by 102Dementia and Cognitive Impairment ResearchMultimodal Machine Learning ApplicationsMachine Learning in Healthcare

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Cascaded Multi-Modal Mixing Transformers for Alzheimer’s Disease Classification with Incomplete Data

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Liu, Linfeng, Liu, Siyu, Zhang, Lu, To, Xuan Vinh, Nasrallah, Fatima, Chandra, Shekhar S.·2023·NeuroImage

DOI 10.1016/j.neuroimage.2023.120267

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Abstract

Accurate medical classification requires a large number of multi-modal data, and in many cases, different feature types. Previous studies have shown promising results when using multi-modal data, outperforming single-modality models when classifying diseases such as Alzheimer's Disease (AD). However, those models are usually not flexible enough to handle missing modalities. Currently, the most common workaround is discarding samples with missing modalities which leads to considerable data under-utilisation. Adding to the fact that labelled medical images are already scarce, the performance of data-driven methods like deep learning can be severely hampered. Therefore, a multi-modal method that can handle missing data in various clinical settings is highly desirable. In this paper, we present Multi-Modal Mixing Transformer (3MT), a disease classification transformer that not only leverages multi-modal data but also handles missing data scenarios. In this work, we test 3MT for AD and Cognitively normal (CN) classification and mild cognitive impairment (MCI) conversion prediction to progressive MCI (pMCI) or stable MCI (sMCI) using clinical and neuroimaging data. The model uses a novel Cascaded Modality Transformers architecture with cross-attention to incorporate multi-modal information for more informed predictions. We propose a novel modality dropout mechanism to ensure an unprecedented level of modality independence and robustness to handle missing data scenarios. The result is a versatile network that enables the mixing of arbitrary numbers of modalities with different feature types and also ensures full data utilization in missing data scenarios. The model is trained and evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with the state-of-the-art performance and further evaluated with The Australian Imaging Biomarker & Lifestyle Flagship Study of Ageing (AIBL) dataset with missing data.

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Authors: Liu, L., Liu, S., Zhang, L., To, X. V., Nasrallah, F., & Chandra, S. S.
Affiliations: The University of Queensland (AU)
Corresponding author: Liu, Linfeng
ORCID: Liu, Linfeng
Liu, Siyu
Zhang, Lu
To, Xuan Vinh
Nasrallah, Fatima
Chandra, Shekhar S.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2023-08
Volume: 277
Pages: 120267
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2023.120267
OpenAlex: W4383315874
Cited by: 101
References: 58
Citations by year: 2026: 23 · 2025: 38 · 2024: 32 · 2023: 7
FWCI: 20.27
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: NeuroImage (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
arXiv (Cornell University)
Funders: U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH-12, AG024904)
Alzheimer's Association (U01 AG024904, W81XWH-12-2-0012)
Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
University of Southern California (W81XWH-12-2-0012, U01 AG024904)
F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-)
BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
Northern California Institute for Research and Education (U01 AG024904, W81XWH-12-2-0012)
Queensland Government (2014000857)
Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
H. Lundbeck A/S (U01 AG024904, AG024904, W81XWH-12-2-0012)
Motor Accident Insurance Commission (2014000857, AG024904)
National Institutes of Health (5u01ag024904-10, W81XWH, W81XWH-12-2-0012)
Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
National Institute on Aging (U01-AG-024904)
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords: Alzheimer's disease classification, multimodal fusion, missing data, modality dropout, cross-attention, neuroimaging data, clinical data, ADNI, AIBL
MeSH terms: Alzheimer Disease, Australia, Humans, Magnetic Resonance Imaging, Biomarkers, Neuroimaging, Cognitive Dysfunction

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Cascaded Multi-Modal Mixing Transformers for Alzheimer’s Disease Classification with Incomplete Data

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