Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs
Continuous-time dynamic graphs naturally abstract many real-world systems, such as social
and transactional networks. While the research on continuous-time dynamic graph …
and transactional networks. While the research on continuous-time dynamic graph …
Known operator learning and hybrid machine learning in medical imaging—a review of the past, the present, and the future
In this article, we perform a review of the state-of-the-art of hybrid machine learning in
medical imaging. We start with a short summary of the general developments of the past in …
medical imaging. We start with a short summary of the general developments of the past in …
Attention guided neural ODE network for breast tumor segmentation in medical images
J Ru, B Lu, B Chen, J Shi, G Chen, M Wang… - Computers in Biology …, 2023 - Elsevier
Breast cancer is the most common cancer in women. Ultrasound is a widely used screening
tool for its portability and easy operation, and DCE-MRI can highlight the lesions more …
tool for its portability and easy operation, and DCE-MRI can highlight the lesions more …
ReconFormer: Accelerated MRI reconstruction using recurrent transformer
The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging
ill-posed inverse problem due to the excessive under-sampling operation in-space. In this …
ill-posed inverse problem due to the excessive under-sampling operation in-space. In this …
Over-and-under complete convolutional rnn for mri reconstruction
Reconstructing magnetic resonance (MR) images from under-sampled data is a challenging
problem due to various artifacts introduced by the under-sampling operation. Recent deep …
problem due to various artifacts introduced by the under-sampling operation. Recent deep …
Multi-scale neural odes for 3d medical image registration
Image registration plays an important role in medical image analysis. Conventional
optimization based methods provide an accurate estimation due to the iterative process at …
optimization based methods provide an accurate estimation due to the iterative process at …
MetaNODE: Prototype optimization as a neural ODE for few-shot learning
Abstract Few-Shot Learning (FSL) is a challenging task, ie, how to recognize novel classes
with few examples? Pre-training based methods effectively tackle the problem by pre …
with few examples? Pre-training based methods effectively tackle the problem by pre …
Pyramid convolutional RNN for MRI image reconstruction
Fast and accurate MRI image reconstruction from undersampled data is crucial in clinical
practice. Deep learning based reconstruction methods have shown promising advances in …
practice. Deep learning based reconstruction methods have shown promising advances in …
Galaxy morphology classification using neural ordinary differential equations
We introduce a continuous depth version of the Residual Network (ResNet) called Neural
ordinary differential equations (NODE) for the purpose of galaxy morphology classification …
ordinary differential equations (NODE) for the purpose of galaxy morphology classification …
Label-free physics-informed image sequence reconstruction with disentangled spatial-temporal modeling
Traditional approaches to image reconstruction uses physics-based loss with data-efficient
inference, although the difficulty to properly model the inverse solution precludes learning …
inference, although the difficulty to properly model the inverse solution precludes learning …