mlsanityData to 14 Oct 2026

GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT

arXiv 2605.22619 ↗First posted 21 May 2026cs.CVcited by 1

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Abstract (arXiv)

Grounding radiology report descriptions to 3D CT volumes is essential for verifiable clinical interpretation, yet remains challenging due to the semantic-spatial gap between free-text narratives and volumetric anatomy. Existing report-assisted and vision-language grounding methods typically rely on phrase-level alignment or dense pixel supervision, resulting in limited lesion-wise correspondence and suboptimal localization accuracy. We propose GLeVE, a graph-guided lesion grounding framework with anatomical prior verification and octree-based autoregressive refinement. GLeVE treats each lesion description as an atomic semantic unit and encodes organ attribution, attributes, and inter-lesion relations through relation-aware graph reasoning to produce discriminative lesion-wise queries. Anatomy-aware proposal generation with region-level verification enforces one-to-one text-lesion alignment, while hierarchical octree refinement progressively improves boundary delineation. Experiments on AbdomenAtlas 3.0 demonstrate consistent gains over classical multimodal foundation models and report-supervised baselines in both segmentation accuracy and lesion-level localization.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 21 May 2026.

Builds on

21 of 32 references.

References · 21

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2025Qwen3 Technical Reportcited by 8,820
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  12. 2023Visual Grounding of Whole Radiology Reports for 3D CT Imagescited by 13
9 more
  1. 2024Uncertainty-aware Medical Diagnostic Phrase Identification and Groundingcited by 13
  2. 2023SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentationcited by 12
  3. 2025Learning Segmentation from Radiology Reportscited by 11
  4. 2024Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Groundingcited by 9
  5. 2024A multi-modal vision-language model for generalizable annotation-free pathology localizationcited by 7
  6. 2024Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Modelscited by 7
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  8. 2025MMLNB: Multi-Modal Learning for Neuroblastoma Subtyping Classification Assisted with Textual Description Generationcited by 3
  9. 2025Generate to Ground: Multimodal Text Conditioning Boosts Phrase Grounding in Medical Vision-Language Modelscited by 2

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Volumetric Radiology AI in the Era of Multimodal Large Language Modelscited by 0