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pith:ZSMB6XCA

pith:2026:ZSMB6XCA7TCO5NFJTUNXA6C2EL
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UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation

Jinghao Shi, Kun Sun, Shuokun Cheng

UHR-Net refines lesion boundaries by splitting hyperedge prototypes according to an entropy uncertainty map.

arxiv:2604.28095 v2 · 2026-04-30 · cs.CV

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4 Citations open
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Claims

C1strongest claim

By splitting hyperedge prototypes into foreground and background groups, UGHR decouples higher-order interactions and improves refinement in ambiguous regions. Experiments on five public benchmarks demonstrate consistent gains over strong baselines.

C2weakest assumption

That an entropy-based uncertainty map derived from a coarse probability map reliably identifies ambiguous regions and that guiding hypergraph refinement with it will not propagate errors from the initial coarse prediction.

C3one line summary

UHR-Net improves medical lesion segmentation accuracy by using uncertainty-guided hypergraph refinement and instance contrastive pretraining to better handle ambiguous boundaries and small lesions.

Receipt and verification
First computed 2026-06-10T01:10:02.421555Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

cc981f5c40fcc4eeb4a99d1b70785a22d4a9525bd1464db28afa0b33105ff204

Aliases

arxiv: 2604.28095 · arxiv_version: 2604.28095v2 · doi: 10.48550/arxiv.2604.28095 · pith_short_12: ZSMB6XCA7TCO · pith_short_16: ZSMB6XCA7TCO5NFJ · pith_short_8: ZSMB6XCA
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZSMB6XCA7TCO5NFJTUNXA6C2EL \
  | 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: cc981f5c40fcc4eeb4a99d1b70785a22d4a9525bd1464db28afa0b33105ff204
Canonical record JSON
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-04-30T16:38:51Z",
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