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This is a Quantitative Study study (also classified as: Experimental Study).

October 13, 2025Open Access

MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencoder

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This method shows an overall improvement of 10.1 in the Dice score, enhancing performance in downstream tasks.
The masked autoencoder framework allows for effective multi-modal learning by treating each MRI sequence as a separate modality.
Our approach enhances the model's ability to recover missing input sequences using cross-sequence reasoning.
The flexible encoder design demonstrates robust performance against an MAE-ViT baseline, proving its generalizability.

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Masked autoencoder enhances segmentation and classification in brain MRIs, indicating improved handling of missing input sequences.

Erdur et al. (2025) studied this question.

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