Search Results for author:
Found 13 papers, 11 papers with code
Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series
The placenta plays a crucial role in fetal development.
Boundary-weighted logit consistency improves calibration of segmentation networks
Neural network prediction probabilities and accuracy are often only weakly-correlated.
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.
Sampling possible reconstructions of undersampled acquisitions in MR imaging
In this paper, we propose a method that instead returns multiple images which are possible under the acquisition model and the chosen prior to capture the uncertainty in the inversion process.
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.
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.
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.
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.
Semi-Supervised and Task-Driven Data Augmentation
However, there is potential to improve the approach by (i) explicitly modeling deformation fields (non-affine spatial transformation) and intensity transformations and (ii) leveraging unlabelled data during the generative process.
A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols
We evaluate the method for brain structure segmentation in MR images.
Temporal Interpolation via Motion Field Prediction
Temporal interpolation of navigator slices an be used to reduce the number of navigator acquisitions without degrading specificity in stacking.
