GLeVE introduces graph-guided lesion grounding with anatomical verification and octree refinement to improve text-to-lesion alignment in 3D CT volumes.
arXiv preprint arXiv:2511.19046 (2025)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.CV 3years
2026 3roles
method 1polarities
use method 1representative citing papers
Existing visual attribution methods often fail to identify the visual evidence used by LVLMs in chest X-ray reasoning, while MedFocus using unbalanced optimal transport and targeted interventions substantially outperforms them across multiple models and settings.
citing papers explorer
-
GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT
GLeVE introduces graph-guided lesion grounding with anatomical verification and octree refinement to improve text-to-lesion alignment in 3D CT volumes.
-
Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models
Existing visual attribution methods often fail to identify the visual evidence used by LVLMs in chest X-ray reasoning, while MedFocus using unbalanced optimal transport and targeted interventions substantially outperforms them across multiple models and settings.
- MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution