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Found 5 papers, 4 papers with code
When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT
First, we find that classical priors are superior to diffusion priors when the number of projections is ``sufficient''.
Regression Conformal Prediction under Bias
Specifically for absolute residual and quantile-based non-conformity scores, we prove: 1) the upper bound of symmetrically adjusted interval lengths increases by $2|b|$ where $b$ is a globally applied scalar value representing bias, 2) asymmetrically adjusted interval lengths are not affected by bias, and 3) conditions when asymmetrically adjusted interval lengths are guaranteed to be smaller than symmetric ones.
Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation
This work applied the Mahalanobis distance (MD) post hoc to the bottleneck features of four Swin UNETR and nnU-net models that segmented the liver on T1-weighted magnetic resonance imaging and computed tomography.
Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation
Clinically deployed segmentation models are known to fail on data outside of their training distribution.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
