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Found 11 papers, 6 papers with code
WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration
Deep image registration has demonstrated exceptional accuracy and fast inference.
Development of Automated Neural Network Prediction for Echocardiographic Left ventricular Ejection Fraction
Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values.
Decoder-Only Image Registration
For this, we propose a novel network architecture, termed LessNet in this paper, which contains only a learnable decoder, while entirely omitting the utilization of a learnable encoder.
Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems
The Alternating Direction Method of Multipliers (ADMM) has gained significant attention across a broad spectrum of machine learning applications.
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration
Instead of directly predicting a full-resolution displacement field, our Fourier-Net learns a low-dimensional representation of the displacement field in the band-limited Fourier domain which our model-driven decoder converts to a full-resolution displacement field in the spatial domain.
Fourier-Net: Fast Image Registration with Band-limited Deformation
Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain.
Ranked #3 on
Medical Image Registration
on OASIS
(val dsc metric)
U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?
On the public 3D IXI brain dataset for atlas-based registration, we show that the performance of the vanilla U-Net is already comparable with that of state-of-the-art transformer-based networks (such as TransMorph), and that the proposed LKU-Net outperforms TransMorph by using only 1. 12% of its parameters and 10. 8% of its mult-adds operations.
SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer
The network was trained and tested on slides from The Cancer Genome Atlas (TCGA) in addition to two independent test datasets.
Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations
The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
A new nonlocal forward model for diffuse optical tomography
The forward model in diffuse optical tomography (DOT) describes how light propagates through a turbid medium.
Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography
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 (DOT) image reconstruction (inverse problem) due to complex and unstructured geometries, non-linearity of the data fitting and regularization terms, and non-differentiability of the regularization term.
