12 citations · 21 across the 14 of their papers we have counts for
14 papers
PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation
Ao Chang, Xing Tao, Xin Yang +5
Interactive medical image segmentation refers to the accurate segmentation of the target of interest through interaction (e.g., click) between the user and the image. It has been w…
Hierarchical Agent-based Reinforcement Learning Framework for Automated Quality Assessment of Fetal Ultrasound Video
Sijing Liu, Qilong Ying, Shuangchi He +3
Ultrasound is the primary modality to examine fetal growth during pregnancy, while the image quality could be affected by various factors. Quality assessment is essential for contr…
Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound
Zehui Lin, Ruobing Huang, Dong Ni +2
Breast cancer is one of the leading causes of cancer deaths in women. As the primary output of breast screening, breast ultrasound (US) video contains exclusive dynamic information…
Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images
Van T. Manh, Jianqiao Zhou, Xiaohong Jia +7
Ultrasound (US) is the primary imaging technique for the diagnosis of thyroid cancer. However, accurate identification of nodule malignancy is a challenging task that can elude les…
Personalized Diagnostic Tool for Thyroid Cancer Classification using Multi-view Ultrasound
Han Huang, Yijie Dong, Xiaohong Jia +4
Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinica…
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…
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…
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
Xin Yang, Yuhao Huang, Ruobing Huang +10
3D ultrasound (US) has become prevalent due to its rich spatial and diagnostic information not contained in 2D US. Moreover, 3D US can contain multiple standard planes (SPs) in one…
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…
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…
Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound
Wang Jian, Miao Juzheng, Yang Xin +9
Breast cancer is the most common invasive cancer in women. Besides the primary B-mode ultrasound screening, sonographers have explored the inclusion of Doppler, strain and shear-wa…
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…
Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks
Felipe Moser, Ruobing Huang, Aris T. Papageorghiou +2
To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain deve…