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.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
TabPFN on radiomic features matched or outperformed image foundation models for IDH mutational status prediction in glioma MRI, with BiomedCLIP strongest among visual encoders and performance sensitive to cohort shifts and calibration.
Empirical comparison of graded MRI preprocessing levels for MAE and JEPA pretraining on brain scans shows moderate levels (P2) are often sufficient, with limited additional utility from stronger preprocessing on downstream tasks.
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How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Models
Empirical comparison of graded MRI preprocessing levels for MAE and JEPA pretraining on brain scans shows moderate levels (P2) are often sufficient, with limited additional utility from stronger preprocessing on downstream tasks.