798 citations · 954 across the 12 of their papers we have counts for
13 papers
Pretraining Deformable Image Registration Networks with Random Images
Junyu Chen, Shuwen Wei, Yihao Liu +2
Recent advances in deep learning-based medical image registration have shown that training deep neural networks~(DNNs) does not necessarily require medical images. Previous work sh…
Deep learning-derived arterial input function for dynamic brain PET
Junyu Chen, Zirui Jiang, Jennifer M. Coughlin +4
Dynamic positron emission tomography (PET) imaging combined with radiotracer kinetic modeling is a powerful technique for visualizing biological processes in the brain, offering va…
Beyond the LUMIR challenge: The pathway to foundational registration models
Junyu Chen, Shuwen Wei, Joel Honkamaa +33
Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benchmarks. Image registration, a foundati…
Correlation Ratio for Unsupervised Learning of Multi-modal Deformable Registration
Xiaojian Chen, Yihao Liu, Shuwen Wei +3
In recent years, unsupervised learning for deformable image registration has been a major research focus. This approach involves training a registration network using pairs of movi…
Unsupervised learning of spatially varying regularization for diffeomorphic image registration
Junyu Chen, Shuwen Wei, Yihao Liu +5
Spatially varying regularization accommodates the deformation variations that may be necessary for different anatomical regions during deformable image registration. Historically,…
Unsupervised Learning of Multi-modal Affine Registration for PET/CT
Junyu Chen, Yihao Liu, Shuwen Wei +2
Affine registration plays a crucial role in PET/CT imaging, where aligning PET with CT images is challenging due to their respective functional and anatomical representations. Desp…
From Registration Uncertainty to Segmentation Uncertainty
Junyu Chen, Yihao Liu, Shuwen Wei +3
Understanding the uncertainty inherent in deep learning-based image registration models has been an ongoing area of research. Existing methods have been developed to quantify both…
A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond
Junyu Chen, Yihao Liu, Shuwen Wei +5
Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as regression-based and U-Net-bas…
Deformable Cross-Attention Transformer for Medical Image Registration
Junyu Chen, Yihao Liu, Yufan He +1
Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration. Recent advancem…
Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration
Junyu Chen, Yihao Liu, Yufan He +1
In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions. However, deep learn…
TransMorph: Transformer for unsupervised medical image registration
Junyu Chen, Eric C. Frey, Yufan He +3
In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by…
From Continuity to Editability: Inverting GANs with Consecutive Images
Yangyang Xu, Yong Du, Wenpeng Xiao +2
Existing GAN inversion methods are stuck in a paradox that the inverted codes can either achieve high-fidelity reconstruction, or retain the editing capability. Having only one of…
ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration
Junyu Chen, Yufan He, Eric C. Frey +2
In the last decade, convolutional neural networks (ConvNets) have dominated and achieved state-of-the-art performances in a variety of medical imaging applications. However, the pe…