pith:IW2PKIDA
BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability
A single pretrained model analyzes brain images using whatever MRI or PET scans are available at the time.
arxiv:2605.13059 v1 · 2026-05-13 · cs.CV
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Claims
Across four downstream tasks and five clinically motivated modality settings, BrainAnytime largely outperforms modality-specific models, missing-modality baselines, and large-scale brain MRI pretrained foundation models on most modality settings. Notably, it surpasses the strongest missing-modality baselines with relative improvements of 6.2% and 7.0% in average accuracy on CN vs. AD and CN vs. MCI classification, respectively.
The pretraining on the five chosen datasets produces representations that generalize to arbitrary unseen modality combinations and to new patient populations without retraining or fine-tuning.
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.
References
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| First computed | 2026-05-18T03:08:59.133852Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
45b4f5206017c8bef1856fc7ab6810014eb8313db0ee7a97e613e57ebd6ef67a
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/IW2PKIDAC7EL54MFN7D2W2AQAF \
| jq -c '.canonical_record' \
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# expect: 45b4f5206017c8bef1856fc7ab6810014eb8313db0ee7a97e613e57ebd6ef67a
Canonical record JSON
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