The C-Score quantifies intra-class explanation consistency for CAM methods via confidence-weighted pairwise soft IoU and detects AUC-consistency dissociation as an early warning for model instability on chest X-ray classification.
AI for radiographic COVID-19 detection selects short- cuts over signal.Nature Machine Intelligence, 3:610– 619
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Neuro-JEPA is a sparse multimodal foundation model pretrained on 1,551,862 brain MRI scans that shows stronger and more consistent performance than existing models and CNN baselines across 47 tasks from clinical and public datasets.
Generative AI must be evaluated as recursive pluralist sociotechnical systems via MaSH Loops and distributional World Values Benchmarks instead of static functionalist or prescriptive tests.
Lung segmentation is necessary for reliable COVID-19 X-ray classification while excessive data augmentation leads to overfitting, with the proposed SDL-COVID method reaching 95.21% precision and low false negatives.
citing papers explorer
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Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification
The C-Score quantifies intra-class explanation consistency for CAM methods via confidence-weighted pairwise soft IoU and detects AUC-consistency dissociation as an early warning for model instability on chest X-ray classification.
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Learning Sparse Latent Predictive Foundation Model for Multimodal Neuroimaging
Neuro-JEPA is a sparse multimodal foundation model pretrained on 1,551,862 brain MRI scans that shows stronger and more consistent performance than existing models and CNN baselines across 47 tasks from clinical and public datasets.
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Measuring the Machine: Evaluating Generative AI as Pluralist Sociotechical Systems
Generative AI must be evaluated as recursive pluralist sociotechnical systems via MaSH Loops and distributional World Values Benchmarks instead of static functionalist or prescriptive tests.
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Are Data Augmentation and Segmentation Always Necessary? Insights from COVID-19 X-Rays and a Methodology Thereof
Lung segmentation is necessary for reliable COVID-19 X-ray classification while excessive data augmentation leads to overfitting, with the proposed SDL-COVID method reaching 95.21% precision and low false negatives.