Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
Tustison, Sohil H
2 Pith papers cite this work, alongside 840 external citations. Polarity classification is still indexing.
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CISR-Net achieves SOTA echocardiography segmentation by fusing local transition probability correlations for semantic rectification and frequency-domain denoising pre-training.
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Fairboard: a quantitative framework for equity assessment of healthcare models
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
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Automatic Echocardiography Segmentation via Transition Probability Correlation for Stable Semantic Extraction
CISR-Net achieves SOTA echocardiography segmentation by fusing local transition probability correlations for semantic rectification and frequency-domain denoising pre-training.