175 citations · 275 across the 32 of their papers we have counts for
32 papers
Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation
Zefan Yang, Di Lin, Dong Ni +1
Scribble-supervised medical image segmentation tackles the limitation of sparse masks. Conventional approaches alternate between: labeling pseudo-masks and optimizing network param…
Online Reflective Learning for Robust Medical Image Segmentation
Yuhao Huang, Xin Yang, Xiaoqiong Huang +7
Deep segmentation models often face the failure risks when the testing image presents unseen distributions. Improving model robustness against these risks is crucial for the large-…
Fine-grained Correlation Loss for Regression
Chaoyu Chen, Xin Yang, Ruobing Huang +10
Regression learning is classic and fundamental for medical image analysis. It provides the continuous mapping for many critical applications, like the attribute estimation, object…
HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images
Jikuan Qian, Rui Li, Xin Yang +8
Different from handcrafted features, deep neural networks can automatically learn task-specific features from data. Due to this data-driven nature, they have achieved remarkable su…
AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images
Kai-Ni Wang, Xin Yang, Juzheng Miao +7
Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology information (scar and edema) to diagnose myocardial infarction. However, automatic pathology segmentati…
Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation
Zefan Yang, Di Lin, Dong Ni +1
Automatic segmentation of abdominal organs in computed tomography (CT) images can support radiation therapy and image-guided surgery workflows. Developing of such automatic solutio…
Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization
Ziqi Zhou, Lei Qi, Xin Yang +2
For medical image segmentation, imagine if a model was only trained using MR images in source domain, how about its performance to directly segment CT images in target domain? This…
Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction
Mingyuan Luo, Xin Yang, Xiaoqiong Huang +6
3D ultrasound (US) is widely used for its rich diagnostic information. However, it is criticized for its limited field of view. 3D freehand US reconstruction is promising in addres…
Flip Learning: Erase to Segment
Yuhao Huang, Xin Yang, Yuxin Zou +7
Nodule segmentation from breast ultrasound images is challenging yet essential for the diagnosis. Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome…
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound
Shuangchi He, Zehui Lin, Xin Yang +13
Standard plane recognition plays an important role in prenatal ultrasound (US) screening. Automatically recognizing the standard plane along with the corresponding anatomical struc…
Style Curriculum Learning for Robust Medical Image Segmentation
Zhendong Liu, Van Manh, Xin Yang +6
The performance of deep segmentation models often degrades due to distribution shifts in image intensities between the training and test data sets. This is particularly pronounced…
Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences
Zejian Chen, Wei Zhuo, Tianfu Wang +2
In this work, we propose a novel straightforward method for medical volume and sequence segmentation with limited annotations. To avert laborious annotating, the recent success of…
Joint Landmark and Structure Learning for Automatic Evaluation of Developmental Dysplasia of the Hip
Xindi Hu, Limin Wang, Xin Yang +9
The ultrasound (US) screening of the infant hip is vital for the early diagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDH refers to measuring alpha and…
Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images
Guang-Quan Zhou, Juzheng Miao, Xin Yang +7
Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have imp…
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound
Xin Yang, Haoran Dou, Ruobing Huang +9
Accurate standard plane (SP) localization is the fundamental step for prenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collected to determine the clinical diagno…
Generalize Ultrasound Image Segmentation via Instant and Plug & Play Style Transfer
Zhendong Liu, Xiaoqiong Huang, Xin Yang +8
Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis. Retraining models leads to high latency and complex…
Contrastive Rendering for Ultrasound Image Segmentation
Haoming Li, Xin Yang, Jiamin Liang +12
Ultrasound (US) image segmentation embraced its significant improvement in deep learning era. However, the lack of sharp boundaries in US images still remains an inherent challenge…
Style-invariant Cardiac Image Segmentation with Test-time Augmentation
Xiaoqiong Huang, Zejian Chen, Xin Yang +5
Deep models often suffer from severe performance drop due to the appearance shift in the real clinical setting. Most of the existing learning-based methods rely on images from mult…
Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network
Junxiong Yu, Chaoyu Chen, Xin Yang +4
Automated breast ultrasound (ABUS) is a new and promising imaging modality for breast cancer detection and diagnosis, which could provide intuitive 3D information and coronal plane…
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
Yuhao Huang, Xin Yang, Rui Li +10
3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Automated standard plane (SP) localization in US volume not only improves effici…
Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes
Xin Yang, Xu Wang, Yi Wang +6
Background and Objective: Biometric measurements of fetal head are important indicators for maternal and fetal health monitoring during pregnancy. 3D ultrasound (US) has unique adv…
A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI
Haoran Dou, Davood Karimi, Caitlin K. Rollins +7
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinem…
Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANs
Jiamin Liang, Xin Yang, Haoming Li +10
Ultrasound (US) is widely accepted in clinic for anatomical structure inspection. However, lacking in resources to practice US scan, novices often struggle to learn the operation s…
Region Proposal Network with Graph Prior and IoU-Balance Loss for Landmark Detection in 3D Ultrasound
Chaoyu Chen, Xin Yang, Ruobing Huang +10
3D ultrasound (US) can facilitate detailed prenatal examinations for fetal growth monitoring. To analyze a 3D US volume, it is fundamental to identify anatomical landmarks of the e…