Search Results for author:
Found 9 papers, 3 papers with code
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability
Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively.
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior
This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets.
Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation
To address this problem, we propose a deep learning-based Bayesian framework, which jointly models image and label statistics, utilizing the domain-irrelevant contour of a medical image for segmentation.
Bayesian Image Super-Resolution with Deep Modeling of Image Statistics
In this work, we propose a Bayesian image restoration framework, where natural image statistics are modeled with the combination of smoothness and sparsity priors.
A low-rank representation for unsupervised registration of medical images
These methods estimate the parameterized transformations between pairs of moving and fixed images through the optimization of the network parameters during training.
VSpSR: Explorable Super-Resolution via Variational Sparse Representation
Super-resolution (SR) is an ill-posed problem, which means that infinitely many high-resolution (HR) images can be degraded to the same low-resolution (LR) image.
Rank-One Network: An Effective Framework for Image Restoration
The RO decomposition is developed to decompose a corrupted image into the RO components and residual.
Multi-scale deep neural networks for real image super-resolution
Single image super-resolution (SR) is extremely difficult if the upscaling factors of image pairs are unknown and different from each other, which is common in real image SR. To tackle the difficulty, we develop two multi-scale deep neural networks (MsDNN) in this work.
