A consensus-based catalog of 18 validation pitfalls, with evidence that common practices understate uncertainty, hide failures, and flip algorithm rankings in surgical video AI.
Common limitations of image processing metrics: A picture story
6 Pith papers cite this work, alongside 87 external citations. Polarity classification is still indexing.
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An adaptation of Confident Learning detects directional label errors in segmentation datasets without clean ground truth and leverages encoder feature separability to mitigate bias and equalize performance across subgroups.
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.
MONAI is a community-supported PyTorch framework that extends deep learning to medical data with domain-specific architectures, transforms, and deployment tools.
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.
Pre-training nnU-Net and MedNeXt on BraTS 2025 data then fine-tuning on BraTS-Africa with added topology refinement yields NSD scores of 0.810, 0.829, and 0.895 for SNFH, NETC, and ET.
citing papers explorer
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Current validation practice undermines surgical AI development
A consensus-based catalog of 18 validation pitfalls, with evidence that common practices understate uncertainty, hide failures, and flip algorithm rankings in surgical video AI.
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Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
An adaptation of Confident Learning detects directional label errors in segmentation datasets without clean ground truth and leverages encoder feature separability to mitigate bias and equalize performance across subgroups.
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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.
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MONAI: An open-source framework for deep learning in healthcare
MONAI is a community-supported PyTorch framework that extends deep learning to medical data with domain-specific architectures, transforms, and deployment tools.
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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.
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Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset
Pre-training nnU-Net and MedNeXt on BraTS 2025 data then fine-tuning on BraTS-Africa with added topology refinement yields NSD scores of 0.810, 0.829, and 0.895 for SNFH, NETC, and ET.