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Found 2 papers, 2 papers with code
Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal Segmentation
Our proposed model outperformed the semi-supervised model in experiments with the ProstateX dataset and an external test set, by leveraging only a subset of unlabeled data rather than the full collection of 4953 cases, our proposed model demonstrated improved performance.
Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI $-$Should Different Clinical Objectives Mandate Different Loss Functions?
We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, require different loss functions for optimal performance due to inherent differences in their clinical objectives.
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