pith:IJNFNYZO
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework
Graph-based framework with region annotations generates more reliable reports from 3D PET/CT scans.
arxiv:2604.18145 v2 · 2026-04-20 · cs.CV · cs.AI
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Our framework achieves SOTA performance, surpassing existing models by 19.7% in BLEU and 4.7% in ROUGE-L, while achieving a remarkable 45.8% improvement in clinical metrics, indicating enhanced clinical reliability and reduced hallucination.
The assumption that graph-based relational modules accurately capture clinically meaningful dependencies between RoI attributes and that LLM-based extraction for RoI Coverage and RoI Quality Index provides an unbiased measure of report fidelity.
Introduces the first large-scale 3D PET/CT dataset with fine-grained RoI annotations for Vietnamese and a graph-enhanced HiRRA framework that achieves SOTA report generation by modeling RoI dependencies.
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| First computed | 2026-05-20T00:01:41.513560Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/IJNFNYZOO25MTWOUIPBQ2TNXCW \
| jq -c '.canonical_record' \
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Canonical record JSON
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