For MAE and JEPA brain MRI pretraining, moving from the cheapest stable preprocessing level (P2) to the best heavier pipeline buys only 3.4 and 1.8 percentage points of aggregate downstream utility, and most gains are statistically unresolved.
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3 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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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.
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.
citing papers explorer
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How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Models
For MAE and JEPA brain MRI pretraining, moving from the cheapest stable preprocessing level (P2) to the best heavier pipeline buys only 3.4 and 1.8 percentage points of aggregate downstream utility, and most gains are statistically unresolved.
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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.
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A Benchmark of (MRI-) Foundation Models to Predict IDH Mutational Status in Glioma
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.