LegSegNet is the first public end-to-end deep learning system for lower extremity CT tissue segmentation and body composition quantification, reporting an average Dice score of 89.31 on held-out test slices.
Mri- core: a foundation model for magnetic resonance imaging
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
Radiomics TabPFN matches or outperforms image foundation models for IDH prediction in glioma MRI, with results sensitive to cohort shifts and representation type.
citing papers explorer
-
LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification
LegSegNet is the first public end-to-end deep learning system for lower extremity CT tissue segmentation and body composition quantification, reporting an average Dice score of 89.31 on held-out test slices.