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Suppressing certain clinical covariates can actually improve the predictive power of prostate MRI grading models, revealing hidden dependencies that could mislead performance assessments.
Explicitly modeling annotator bias and variability can significantly enhance uncertainty calibration in medical image segmentation without sacrificing accuracy.
T2-weighted MRI alone can outperform standard T2w+DWI in prostate cancer localization, thanks to a clever training scheme that leverages DWI as a "privileged" latent modality.
