PRA-PoE aligns representations across modality subsets via learnable prototypes and fuses Gaussian experts with uncertainty-weighted precision to improve Alzheimer's diagnosis under missing data.
In: Workshop on Large Language Models and Generative AI for Health at AAAI 2025 (2025)
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A single pretrained 3D masked autoencoder handles arbitrary combinations of multi-sequence MRI and amyloid-PET for brain analysis by combining cross-modal distillation with atlas-guided curriculum masking and outperforms missing-modality baselines on Alzheimer's classification tasks.
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PRA-PoE: Robust Multimodal Alzheimer's Diagnosis with Arbitrary Missing Modalities
PRA-PoE aligns representations across modality subsets via learnable prototypes and fuses Gaussian experts with uncertainty-weighted precision to improve Alzheimer's diagnosis under missing data.
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BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability
A single pretrained 3D masked autoencoder handles arbitrary combinations of multi-sequence MRI and amyloid-PET for brain analysis by combining cross-modal distillation with atlas-guided curriculum masking and outperforms missing-modality baselines on Alzheimer's classification tasks.