15 citations · 26 across the 7 of their papers we have counts for
7 papers
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration
Xi Jia, Alexander Thorley, Alberto Gomez +3
U-Net style networks are commonly utilized in unsupervised image registration to predict dense displacement fields, which for high-resolution volumetric image data is a resource-in…
Fourier-Net: Fast Image Registration with Band-limited Deformation
Xi Jia, Joseph Bartlett, Wei Chen +6
Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric imag…
U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?
Xi Jia, Joseph Bartlett, Tianyang Zhang +3
Due to their extreme long-range modeling capability, vision transformer-based networks have become increasingly popular in deformable image registration. We believe, however, that…
SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer
Wenqi Lu, Michael Toss, Emad Rakha +2
Human epidermal growth factor receptor 2 (HER2) is an important prognostic and predictive factor which is overexpressed in 15-20% of breast cancer (BCa). The determination of its s…
Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations
Noorul Wahab, Islam M Miligy, Katherine Dodd +24
Recent advances in whole slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence (AI) based diagnostic, prognostic, an…
A new nonlocal forward model for diffuse optical tomography
Wenqi Lu, Jinming Duan, Joshua Deepak Veesa +1
The forward model in diffuse optical tomography (DOT) describes how light propagates through a turbid medium. It is often approximated by a diffusion equation (DE) that is numerica…
Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography
Wenqi Lu, Jinming Duan, David Orive-Miguel +2
Total variation (TV) is a powerful regularization method that has been widely applied in different imaging applications, but is difficult to apply to diffuse optical tomography (DO…