Biomedical Imaging Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification
Gonzalo Esteban, Mosquera Rojas, S. R. van der Voort et al.
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Imaging Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hip-pocampal Segmentation
Ensemble-derived uncertainty quantification with sample variance, mutual information, and predictive entropy is shown to reduce inaccurate segmentations by flagging low-confidence cases, potentially providing a mechanism for automatically escalating ambiguous cases for expert assessment.
Gabriel Oliveira-Stahl, Anna Schroder, James Moggridge et al.
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Latent-to-Latent Flow for Volumetric Stochastic Segmentation
This work introduces a latent-to-latent flow technique for stochastic segmentation of medical volumes via encoded representations of both the image and label space, improving efficiency up to 14x compared with full resolution models while maintaining clinically relevant performance.
Omar Todd, Sooha Kim, Raghav Mehta et al.
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