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Found 4 papers, 1 papers with code
Navigating Uncertainty in Medical Image Segmentation
We address the selection and evaluation of uncertain segmentation methods in medical imaging and present two case studies: prostate segmentation, illustrating that for minimal annotator variation simple deterministic models can suffice, and lung lesion segmentation, highlighting the limitations of the Generalized Energy Distance (GED) in model selection.
That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation
Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set.
Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
Image segmentation relies heavily on neural networks which are known to be overconfident, especially when making predictions on out-of-distribution (OOD) images.
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy
b) We provide a full deep-learning methodology for this difficult noisy task on time-series image data.
