mlsanityData to 14 Oct 2026

Multi-modal Imputation for Alzheimer's Disease Classification

arXiv 2601.21076 ↗First posted 28 Jan 2026cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer's disease, from magnetic resonance imaging (MRI). Combining multiple imaging modalities, such as T1-weighted (T1) and diffusion-weighted imaging (DWI) scans, can increase diagnostic performance. However, complete multimodal datasets are not always available. We use a conditional denoising diffusion probabilistic model to impute missing DWI scans from T1 scans. We perform extensive experiments to evaluate whether such imputation improves the accuracy of uni-modal and bi-modal deep learning models for 3-way Alzheimer's disease classification-cognitively normal, mild cognitive impairment, and Alzheimer's disease. We observe improvements in several metrics, particularly those sensitive to minority classes, for several imputation configurations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 28 references that are in the map. First posted on arXiv 28 Jan 2026.

Builds on

10 of 28 references.

References · 10

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  5. 2017Mixed Precision Trainingcited by 1,156
  6. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
  7. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  8. 2022Brain Imaging Generation with Latent Diffusion Modelscited by 268
  9. 2023Generative AI for Medical Imaging: extending the MONAI Frameworkcited by 68
  10. 2025Diffusion Bridge Models for 3D Medical Image Translationcited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Pretraining Transformer-Based Models on Diffusion-Generated Synthetic Graphs for Alzheimer's Disease Prediction
  2. 2026Adaptive Clinical-Aware Latent Diffusion for Multimodal Brain Image Generation and Missing Modality Imputation
  3. 2025AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation
  4. 2022Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
  5. 2025Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction
  6. 2026Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET
  7. 2025NeuroMoE: A Transformer-Based Mixture-of-Experts Framework for Multi-Modal Neurological Disorder Classification
  8. 2023Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection