Rad-VLSM is a cross-modal two-stage framework that converts semantic guidance from BLIP-2 into box prompts for SAM-based lesion segmentation and then uses the resulting masks as spatial priors in a visual-radiomics fusion head for diagnosis.
Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ASGNet combines a spectrum-guided non-local perception module, multi-source semantic extractor, and dense cross-layer decoder to outperform 21 prior methods on five polyp segmentation benchmarks.
citing papers explorer
-
Rad-VLSM: A Cross-Modal Framework with Semantics-Assisted Prompting for Medical Segmentation and Diagnosis
Rad-VLSM is a cross-modal two-stage framework that converts semantic guidance from BLIP-2 into box prompts for SAM-based lesion segmentation and then uses the resulting masks as spatial priors in a visual-radiomics fusion head for diagnosis.
-
ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentation
ASGNet combines a spectrum-guided non-local perception module, multi-source semantic extractor, and dense cross-layer decoder to outperform 21 prior methods on five polyp segmentation benchmarks.