ArticleInformation processing in medical imaging : proceedings of the ... conference2021
Representation Disentanglement for Multi-modal Brain MRI Analysis.
Article in Information processing in medical imaging : proceedings of the ... conference, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Hierarchical in-out fusion for incomplete multimodal brain tumor segmentation.Scientific reports · 2025Article
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Neurocognitive Latent Space Regularization for Multi-Label Diagnosis from MRI.Predictive Intelligence in Medicine. PRIME (Workshop) · 2025Article
- Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.Neurology · 2024Article
- Domain generalization for retinal vessel segmentation via Hessian-based vector field.Medical image analysis · 2024Article
- Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- HACA3: A unified approach for multi-site MR image harmonization.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2023Article
- Denoising Single Images by Feature Ensemble Revisited.Sensors (Basel, Switzerland) · 2022Article
- Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory.NeuroImage · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Multi-modal MRIs are widely used in neuroimaging applications since different MR sequences provide complementary information about brain structures. Recent works have suggested that multi-modal deep learning analysis can benefit from explicitly disentangling anatomical (shape) and modality (appearance) information into separate image presentations. In this work, we challenge mainstream strategies by showing that they do not naturally lead to representation disentanglement both in theory and in practice. To address this issue, we propose a margin loss that regularizes the similarity in relationships of the representations across subjects and modalities. To enable robust training, we further use a conditional convolution to design a single model for encoding images of all modalities. Lastly, we propose a fusion function to combine the disentangled anatomical representations as a set of modality-invariant features for downstream tasks. We evaluate the proposed method on three multi-modal neuroimaging datasets. Experiments show that our proposed method can achieve superior disentangled representations compared to existing disentanglement strategies. Results also indicate that the fused anatomical representation has potential in the downstream task of zero-dose PET reconstruction and brain tumor segmentation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.