502 citations · 502 across the 6 of their papers we have counts for
8 papers
Recurrent Contrastive Learning for Imbalanced Medical Image Classification
Zhiyuan Zhu, Xinling Meng, Junxuan Yu +15
Medical image classification often suffers from class imbalance due to the inherent disparities in disease incidence. Existing approaches, such as class resampling and loss reweigh…
Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
Jian Wang, Qiongying Ni, Hongkui Yu +15
Accurate fetal birth weight (FBW) estimation is essential for optimizing delivery decisions and reducing perinatal mortality. However, clinical methods for FBW estimation are ineff…
TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
Qi Li, Shaheer U. Saeed, Yuliang Huang +24
Trackerless freehand ultrasound reconstruction aims to reconstruct 3D volumes from sequences of 2D ultrasound images without relying on external tracking systems. By eliminating th…
Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction
Zhongnuo Yan, Xin Yang, Mingyuan Luo +4
Fine-grained spatio-temporal learning is crucial for freehand 3D ultrasound reconstruction. Previous works mainly resorted to the coarse-grained spatial features and the separated…
A Foundation Model for General Moving Object Segmentation in Medical Images
Zhongnuo Yan, Tong Han, Yuhao Huang +7
Medical image segmentation aims to delineate the anatomical or pathological structures of interest, playing a crucial role in clinical diagnosis. A substantial amount of high-quali…
Multi-IMU with Online Self-Consistency for Freehand 3D Ultrasound Reconstruction
Mingyuan Luo, Xin Yang, Zhongnuo Yan +7
Ultrasound (US) imaging is a popular tool in clinical diagnosis, offering safety, repeatability, and real-time capabilities. Freehand 3D US is a technique that provides a deeper un…
Instructive Feature Enhancement for Dichotomous Medical Image Segmentation
Lian Liu, Han Zhou, Jiongquan Chen +5
Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance.…
Segment Anything Model for Medical Images?
Yuhao Huang, Xin Yang, Lian Liu +16
The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. Howev…