Search Results for author:
Found 7 papers, 1 papers with code
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound
In this study, we build a novel multi-label learning (MLL) scheme to identify multiple standard planes and corresponding anatomical structures of fetus simultaneously.
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
First, our proposed method is general and it can accurately localize multiple SPs in different challenging US datasets.
Multi-agent Reinforcement Learning
Neural Architecture Search
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound
2D US has to perform scanning for each SP, which is time-consuming and operator-dependent.
Generalize Ultrasound Image Segmentation via Instant and Plug & Play Style Transfer
Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis.
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
In this study, we propose a novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously.
Multi-agent Reinforcement Learning
Neural Architecture Search
Region Proposal Network with Graph Prior and IoU-Balance Loss for Landmark Detection in 3D Ultrasound
The performance of the proposed framework is evaluated on a 3D US dataset to detect five key fetal facial landmarks.
Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer
In this paper, we propose a novel and intuitive framework to remove the appearance shift, and hence improve the generalization ability of DNNs.
