Pith Number

pith:EE2VZFG7

pith:2026:EE2VZFG7OP62HUQZJRHHKAVLZ4

not attested not anchored not stored refs resolved

PRA-PoE: Robust Alzheimer's Diagnosis with Arbitrary Missing Modalities

Guangqian Yang, Qian Niu, Shujun Wang, Wenlong Hou, Ye Du

PRA-PoE aligns representations across missing modality subsets and fuses uncertain Gaussian experts to improve Alzheimer's diagnosis on incomplete data.

arxiv:2605.13081 v1 · 2026-05-13 · cs.CV

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

PRA-PoE consistently outperforms the state-of-the-art across datasets, achieving a 5.4% relative improvement in average accuracy on ADNI and a 10.9% relative gain in average F1 on OASIS-3 over the strongest baseline across all non-empty modality subsets.

C2weakest assumption

That learnable global prototypes plus availability-conditioned tokens can reliably encode modality presence, re-synthesize missing features, and align latent spaces across subsets without introducing new biases or overfitting to the specific missingness patterns in the training data.

C3one line summary

PRA-PoE combines learnable prototypes for representation alignment across modality subsets with uncertainty-weighted product-of-experts fusion to improve robustness and calibration, reporting 5.4% accuracy gain on ADNI and 10.9% F1 gain on OASIS-3 over baselines across all non-empty modality subsets

References

26 extracted · 26 resolved · 0 Pith anchors

[1] IEEE Open Journal of Engineering in Medicine and Biology6, 183–192 (2024) 2024

[2] Alzheimer’s & Dementia18(4), 561–571 (2022) 2022

[3] IEEE Transactions on Medical Imaging42(12), 3566–3578 (2023) 2023

[4] In: International Con- ference on Medical Image Computing and Computer-Assisted Intervention 2024

[5] Advances in Neural Information Processing Sys- tems37, 67850–67900 (2025) 2025

Receipt and verification

First computed	2026-05-18T03:08:58.684228Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

21355c94df73fda3d2194c4e7502abcf00f2d71d4a87590b29918b92ca0f4e98

Aliases

arxiv: 2605.13081 · arxiv_version: 2605.13081v1 · doi: 10.48550/arxiv.2605.13081 · pith_short_12: EE2VZFG7OP62 · pith_short_16: EE2VZFG7OP62HUQZ · pith_short_8: EE2VZFG7

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/EE2VZFG7OP62HUQZJRHHKAVLZ4 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 21355c94df73fda3d2194c4e7502abcf00f2d71d4a87590b29918b92ca0f4e98

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "10fadce2751fcb9d2f82987b202d232bf7a2e5890e2b37ca55ae0e15b0174e9b",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T06:54:21Z",
    "title_canon_sha256": "c3966583a74b1dffb38fc2ddb936c7d4973a17538cfda3e5c5913d43704ccc02"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13081",
    "kind": "arxiv",
    "version": 1
  }
}