Search Results for author:
Found 8 papers, 5 papers with code
Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation
Accordingly, we propose a contextual embedding learning approach to facilitate 2D CNNs capturing spatial information properly.
ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration
Deformable image registration plays a crucial role in medical imaging, aiding in disease diagnosis and image-guided interventions.
Pyramid Attention Network for Medical Image Registration
The advent of deep-learning-based registration networks has addressed the time-consuming challenge in traditional iterative methods. However, the potential of current registration networks for comprehensively capturing spatial relationships has not been fully explored, leading to inadequate performance in large-deformation image registration. The pure convolutional neural networks (CNNs) neglect feature enhancement, while current Transformer-based networks are susceptible to information redundancy. To alleviate these issues, we propose a pyramid attention network (PAN) for deformable medical image registration. Specifically, the proposed PAN incorporates a dual-stream pyramid encoder with channel-wise attention to boost the feature representation. Moreover, a multi-head local attention Transformer is introduced as decoder to analyze motion patterns and generate deformation fields. Extensive experiments on two public brain magnetic resonance imaging (MRI) datasets and one abdominal MRI dataset demonstrate that our method achieves favorable registration performance, while outperforming several CNN-based and Transformer-based registration networks. Our code is publicly available at https://github. com/JuliusWang-7/PAN.
Physics-Informed Representation and Learning: Control and Risk Quantification
Optimal and safety-critical control are fundamental problems for stochastic systems, and are widely considered in real-world scenarios such as robotic manipulation and autonomous driving.
MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures
An effective representation of drug molecules emerges as a pivotal component in this pursuit.
ACDNet: Attention-guided Collaborative Decision Network for Effective Medication Recommendation
ACDNet also employs a collaborative decision framework, utilizing the similarity between medication records and medicine representation to facilitate the recommendation process.
A Generalizable Physics-informed Learning Framework for Risk Probability Estimation
In this paper, we develop an efficient method to evaluate the probabilities of long-term risk and their gradients.
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning
Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks.
