Pith Number

pith:XFEP5LDT

pith:2026:XFEP5LDTS7V7FKEDBV26TLZG24

not attested not anchored not stored refs resolved

Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor Segmentation with Missing Modalities

Chao Yao, Jiao Pan, Sha Tao, Yu Guo

A graph neural network with virtual nodes and dynamic connections segments brain tumors effectively even with missing MRI modalities.

arxiv:2605.16880 v1 · 2026-05-16 · cs.AI

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{XFEP5LDTS7V7FKEDBV26TLZG24}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

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

Extensive experiments on the BRATS-2018 and BRATS-2020 datasets demonstrate that our method outperforms the state-of-the-art methods on almost all subsets of incomplete modalities.

C2weakest assumption

That dynamically adjusting the adjacency matrix based on modality availability will preserve beneficial information flow while mitigating interference effects caused by missing modalities without introducing new biases or artifacts.

C3one line summary

A one-stage graph framework with modality-specific virtual nodes and dynamic adjacency adjustment for robust brain tumor segmentation under arbitrary missing MRI modalities, outperforming SOTA on BRATS-2018 and BRATS-2020 incomplete subsets.

References

39 extracted · 39 resolved · 4 Pith anchors

[1] Slic superpix- els compared to state-of-the-art superpixel methods.IEEE transactions on pattern analysis and machine intelligence, 34(11):2274–2282, 2012 2012

[2] Smu-net: Style matching u-net for brain tumor segmentation with missing modalities 2022

[3] Robust multimodal brain tumor seg- mentation via feature disentanglement and gated fusion

[4] Learning with privileged multimodal knowledge for unimodal segmentation.IEEE transactions on medical imaging, 41(3):621–632, 2021 2021

[5] Rfnet: Region-aware fusion network for incomplete multi-modal brain tumor seg- mentation 2021

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-20T00:03:27.967246Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b948feac7397ebf2a8830d75e9af26d7333928e82d34206b3eb12fe1bda1ab44

Aliases

arxiv: 2605.16880 · arxiv_version: 2605.16880v1 · doi: 10.48550/arxiv.2605.16880 · pith_short_12: XFEP5LDTS7V7 · pith_short_16: XFEP5LDTS7V7FKED · pith_short_8: XFEP5LDT

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/XFEP5LDTS7V7FKEDBV26TLZG24 \
  | 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: b948feac7397ebf2a8830d75e9af26d7333928e82d34206b3eb12fe1bda1ab44

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "13b3797cf44156e059d126a33d855d48e59e23f67a609e8cf028217a66be8fcd",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-16T08:40:01Z",
    "title_canon_sha256": "f6b75fa6b35a0dac4e56e44259ac79eb0ff381bba4f3fed96c52c80faaa26895"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16880",
    "kind": "arxiv",
    "version": 1
  }
}