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Found 14 papers, 8 papers with code
Explicitly Minimizing the Blur Error of Variational Autoencoders
Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have been trained on.
A Field of Experts Prior for Adapting Neural Networks at Test Time
We use 1D marginal distributions of a trained task CNN's features as experts in the FoE model.
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
In this paper, we propose a local contrastive loss to learn good pixel level features useful for segmentation by exploiting semantic label information obtained from pseudo-labels of unlabeled images alongside limited annotated images.
Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes
Such class imbalance is ubiquitous in clinical applications and very crucial to handle because the classes with fewer samples most often correspond to critical cases (e. g., cancer) where misclassifications can have severe consequences.
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
We incorporate the reversible blocks into a recently proposed architecture called PHiSeg that is developed for uncertainty quantification in medical image segmentation.
Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices.
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task.
Task-agnostic Out-of-Distribution Detection Using Kernel Density Estimation
The results demonstrate that the proposed method consistently achieves high OOD detection performance in both classification and segmentation tasks and improves state-of-the-art in almost all cases.
Contrastive learning of global and local features for medical image segmentation with limited annotations
In this work, we propose strategies for extending the contrastive learning framework for segmentation of volumetric medical images in the semi-supervised setting with limited annotations, by leveraging domain-specific and problem-specific cues.
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
In medical image segmentation, this premise is violated when there is a mismatch between training and test images in terms of their acquisition details, such as the scanner model or the protocol.
Combining nonparametric spatial context priors with nonparametric shape priors for dendritic spine segmentation in 2-photon microscopy images
Data driven segmentation is an important initial step of shape prior-based segmentation methods since it is assumed that the data term brings a curve to a plausible level so that shape and data terms can then work together to produce better segmentations.
Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors
In this paper, we propose an efficient pseudo-marginal Markov chain Monte Carlo (MCMC) sampling approach to draw samples from posterior shape distributions for image segmentation.
MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors
With a statistical view, addressing these issues would involve the problem of characterizing the posterior densities of the shapes of the objects to be segmented.
Dendritic Spine Shape Analysis: A Clustering Perspective
We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem.
