Scoring the same cervical-spine segmentations against silver rather than expert labels overestimates Dice by ~8 points and turns a non-significant age fairness gap into a significant one via variance collapse.
In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
3 Pith papers cite this work. Polarity classification is still indexing.
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SIAM achieves state-of-the-art whole-head MRI segmentation of 16 structures including extra-cerebral tissues by training on synthetic data from just six manual templates, matching or exceeding prior methods on 301 scans across eight heterogeneous datasets.
The autoPET3 challenge finds that leading AI models reach a mean Dice score of 0.66 for multitracer PET/CT lesion segmentation, with compositional generalization to unseen tracer-center pairs remaining an open problem driven by volume overestimation and case heterogeneity.
citing papers explorer
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False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation
Scoring the same cervical-spine segmentations against silver rather than expert labels overestimates Dice by ~8 points and turns a non-significant age fairness gap into a significant one via variance collapse.
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SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Training
SIAM achieves state-of-the-art whole-head MRI segmentation of 16 structures including extra-cerebral tissues by training on synthetic data from just six manual templates, matching or exceeding prior methods on 301 scans across eight heterogeneous datasets.
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The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
The autoPET3 challenge finds that leading AI models reach a mean Dice score of 0.66 for multitracer PET/CT lesion segmentation, with compositional generalization to unseen tracer-center pairs remaining an open problem driven by volume overestimation and case heterogeneity.