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:2203.00131 (2023)
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
CDSA-Net decouples vascular structure extraction and background restoration in coronary DSA via hierarchical geometric priors and adaptive noise modeling to eliminate artifacts while preserving tissue fidelity.
MambaLiteUNet integrates Mamba into U-Net with adaptive fusion, local-global mixing, and cross-gated attention modules to reach 87.12% IoU and 93.09% Dice on skin lesion datasets while cutting parameters by 93.6%.
DSVM-UNet improves VM-UNet by dual self-distillation, reaching state-of-the-art segmentation performance on ISIC2017, ISIC2018, and Synapse datasets.
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.
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.
-
CDSA-Net:Collaborative Decoupling of Vascular Structure and Background for High-Fidelity Coronary Digital Subtraction Angiography
CDSA-Net decouples vascular structure extraction and background restoration in coronary DSA via hierarchical geometric priors and adaptive noise modeling to eliminate artifacts while preserving tissue fidelity.
-
MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation
MambaLiteUNet integrates Mamba into U-Net with adaptive fusion, local-global mixing, and cross-gated attention modules to reach 87.12% IoU and 93.09% Dice on skin lesion datasets while cutting parameters by 93.6%.
-
DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation
DSVM-UNet improves VM-UNet by dual self-distillation, reaching state-of-the-art segmentation performance on ISIC2017, ISIC2018, and Synapse datasets.
-
Data-Centric Foundation Models in Computational Healthcare: A Survey
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.