A volumetric vision-language model trained jointly on classification labels and segmentation masks from three CT datasets reaches 83% AUROC on CT-RATE and shows cross-dataset zero-shot behavior.
Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.Medical image analysis, 67:101857, 2021
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
Unified Supervision For Vision-Language Modeling in 3D Computed Tomography
A volumetric vision-language model trained jointly on classification labels and segmentation masks from three CT datasets reaches 83% AUROC on CT-RATE and shows cross-dataset zero-shot behavior.