pith:ZSMB6XCA
UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation
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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Record completeness
Claims
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.
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.
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
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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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