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.
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
MAML with auxiliary cavity tasks and boundary-aware loss achieves better few-shot 3D left atrial wall segmentation than standard fine-tuning, reaching DSC 0.64 at 5 shots versus 0.52.
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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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Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning
MAML with auxiliary cavity tasks and boundary-aware loss achieves better few-shot 3D left atrial wall segmentation than standard fine-tuning, reaching DSC 0.64 at 5 shots versus 0.52.