Search Results for author:
Found 16 papers, 8 papers with code
From Registration Uncertainty to Segmentation Uncertainty
Here, we propose a novel framework to concurrently estimate both the epistemic and aleatoric segmentation uncertainties for image registration.
One-for-All: Towards Universal Domain Translation with a Single StyleGAN
In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences.
A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond
Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade.
Curricular Contrastive Regularization for Physics-aware Single Image Dehazing
In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one.
Ranked #2 on
Image Dehazing
on SOTS Indoor
Deformable Cross-Attention Transformer for Medical Image Registration
Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration.
Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration
In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions.
Editing Out-of-domain GAN Inversion via Differential Activations
Consequently, in the decomposition phase, we further present a GAN prior based deghosting network for separating the final fine edited image from the coarse reconstruction.
TransMorph: Transformer for unsupervised medical image registration
Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications.
Ranked #1 on
Medical Image Registration
on OASIS
From Continuity to Editability: Inverting GANs with Consecutive Images
This inborn property is used for two unique purposes: 1) regularizing the joint inversion process, such that each of the inverted code is semantically accessible from one of the other and fastened in a editable domain; 2) enforcing inter-image coherence, such that the fidelity of each inverted code can be maximized with the complement of other images.
Learning From the Master: Distilling Cross-Modal Advanced Knowledge for Lip Reading
To these ends, we introduce a trainable "master" network which ingests both audio signals and silent lip videos instead of a pretrained teacher.
Mask-ShadowNet: Towards Shadow Removal via Masked Adaptive Instance Normalization
Shadow removal is an important yet challenging task in image processing and computer vision.
Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks
The results demonstrated that the proposed method provides fast and robust lesion and bone segmentation for QBSPECT/CT.
ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration
However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image.
Ranked #4 on
Medical Image Registration
on OASIS
Invertible Grayscale via Dual Features Ensemble
In this paper, we propose to embed color information into an invertible grayscale, such that it can be easily recovered to the original color.
Generating Anthropomorphic Phantoms Using Fully Unsupervised Deformable Image Registration with Convolutional Neural Networks
In this work, we present a novel image registration method for creating highly anatomically detailed anthropomorphic phantoms from a single digital phantom.
Hierarchical recurrent neural network for skeleton based action recognition
Traditional methods generally model the spatial structure and temporal dynamics of human skeleton with hand-crafted features and recognize human actions by well-designed classifiers.
Ranked #118 on
Skeleton Based Action Recognition
on NTU RGB+D
