Pith Number

pith:2GWQAXOI

pith:2026:2GWQAXOIJPRNPM33SVM73IW5LZ

not attested not anchored not stored refs resolved

Attention-Based Multimodal Survival Prediction with Cross-Modal Bilinear Fusion

Hassan Keshvarikhojasteh, Josien P.W. Pluim, Mitko Veta

A multimodal model fuses histology, RNA-seq, and clinical data with low-rank bilinear pooling to predict survival more accurately than concatenation baselines.

arxiv:2605.13897 v1 · 2026-05-12 · q-bio.QM · cs.LG

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\pithnumber{2GWQAXOIJPRNPM33SVM73IW5LZ}

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1 Bitcoin timestamp

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

Experiments on the CHIMERA challenge dataset demonstrate improved predictive performance over concatenation-based baselines and competitive generalization on hidden evaluation cohorts.

C2weakest assumption

That the low-rank bilinear fusion captures the clinically relevant conditional interactions across histology, RNA-seq, and clinical modalities without discarding important information, and that the Kaplan-Meier calibration step produces well-calibrated survival estimates on the target population.

C3one line summary

A multimodal survival model using attention-based histology features, RNA-seq encoders, and low-rank bilinear fusion shows improved performance over concatenation baselines on the CHIMERA dataset for HR-NMIBC.

References

14 extracted · 14 resolved · 2 Pith anchors

[1] Chimera challenge – combining histology, medical imaging and molecular data for medical prognosis and diagnosis.https://chimera.grand-challenge.org(2025), accessed: 2026-02-04 2025

[2] IEEE Transactions on Pattern Analysis and Machine Intelligence 41(2), 423–443 (2019) 2019

[3] Cheerla, A., Gevaert, O.: Deep learning with multimodal representation for pan- cancer prognosis prediction. Bioinformatics35(14), i446–i454 (2019) 2019

[4] Nature medicine30(3), 850–862 (2024) 2024

[5] IEEE Transactions on Medical Imaging41(4), 757–770 (2020) 2020

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:18.985332Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d1ad005dc84be2d7b37b9559fda2dd5e55e42c537ed7d291907ce93dfa916a8d

Aliases

arxiv: 2605.13897 · arxiv_version: 2605.13897v1 · doi: 10.48550/arxiv.2605.13897 · pith_short_12: 2GWQAXOIJPRN · pith_short_16: 2GWQAXOIJPRNPM33 · pith_short_8: 2GWQAXOI

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/2GWQAXOIJPRNPM33SVM73IW5LZ \
  | 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: d1ad005dc84be2d7b37b9559fda2dd5e55e42c537ed7d291907ce93dfa916a8d

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "f1ba923519daef79e01246f0e449f862e9c62af6dad056a9df744c38ca6d6f94",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "q-bio.QM",
    "submitted_at": "2026-05-12T13:09:25Z",
    "title_canon_sha256": "168bd8626c685ea98bd3dd50b799d539af73de41837a212d8c22a5912370d276"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13897",
    "kind": "arxiv",
    "version": 1
  }
}