TriALS introduces a 150-case four-phase CT dataset and challenge showing top segmentation methods reach 0.754 Dice on venous phase but only 0.57 on non-contrast CT, with external validation gains up to 28%.
Commun.9, 10.1038/s41467-018-07619-7 (2018)
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The FedSurg challenge benchmarks federated learning on appendectomy videos and finds only 26% F1 on unseen centers even with centralized data, plus extra penalties from decentralization, with spatiotemporal models performing best.
NucEval is a unified evaluation framework for nuclear instance segmentation that modifies standard metrics to handle vague regions, normalize scores, manage overlaps, and account for border uncertainty.
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TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT
TriALS introduces a 150-case four-phase CT dataset and challenge showing top segmentation methods reach 0.754 Dice on venous phase but only 0.57 on non-contrast CT, with external validation gains up to 28%.
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Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
The FedSurg challenge benchmarks federated learning on appendectomy videos and finds only 26% F1 on unseen centers even with centralized data, plus extra penalties from decentralization, with spatiotemporal models performing best.
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NucEval: A Robust Evaluation Framework for Nuclear Instance Segmentation
NucEval is a unified evaluation framework for nuclear instance segmentation that modifies standard metrics to handle vague regions, normalize scores, manage overlaps, and account for border uncertainty.