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
Radiomics TabPFN matches or outperforms image foundation models for IDH prediction in glioma MRI, with results sensitive to cohort shifts and representation type.
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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A Benchmark of (MRI-) Foundation Models to Predict IDH Mutational Status in Glioma
Radiomics TabPFN matches or outperforms image foundation models for IDH prediction in glioma MRI, with results sensitive to cohort shifts and representation type.