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.
Gordon, Timothy O
2 Pith papers cite this work. Polarity classification is still indexing.
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Hierarchy-informed curricular optimization of heterogeneous whole-brain models enables generalization to new subjects and prediction of behavioral abilities from parameters.
citing papers explorer
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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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Evolution With Purpose: Hierarchy-Informed Optimization of Whole-Brain Models
Hierarchy-informed curricular optimization of heterogeneous whole-brain models enables generalization to new subjects and prediction of behavioral abilities from parameters.