53 citations · 68 across the 15 of their papers we have counts for
16 papers
PC-Seg: Progressive Cross-View Consistency for 3D OCT Segmentation from Sparse 2D Annotations
Tsubasa Konno, Takahiro Ninomiya, Yukun Zhou +6
Volumetric segmentation of optical coherence tomography (OCT) images is essential for diagnosing ocular diseases but requires labor-intensive voxel-wise annotations. While semi-sup…
Cross-view Generalized Diffusion Model for Sparse-view CT Reconstruction
Jixiang Chen, Yiqun Lin, Yi Qin +2
Sparse-view computed tomography (CT) reduces radiation exposure by subsampling projection views, but conventional reconstruction methods produce severe streak artifacts with unders…
Real-Time Reconstruction of 3D Bone Models via Very-Low-Dose Protocols
Yiqun Lin, Haoran Sun, Yongqing Li +12
Patient-specific bone models are essential for designing surgical guides and preoperative planning, as they enable the visualization of intricate anatomical structures. However, tr…
DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction
Yiqun Lin, Jixiang Chen, Hualiang Wang +4
Cone-beam computed tomography (CBCT) is a critical 3D imaging technology in the medical field, while the high radiation exposure required for high-quality imaging raises significan…
CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos
Jiewen Yang, Yiqun Lin, Bin Pu +3
Echocardiogram video plays a crucial role in analysing cardiac function and diagnosing cardiac diseases. Current deep neural network methods primarily aim to enhance diagnosis accu…
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding
Jiewen Yang, Yiqun Lin, Bin Pu +1
Quantitative analysis of cardiac motion is crucial for assessing cardiac function. This analysis typically uses imaging modalities such as MRI and Echocardiograms that capture deta…
Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images
Hualiang Wang, Yiqun Lin, Xinpeng Ding +1
General networks for 3D medical image segmentation have recently undergone extensive exploration. Behind the exceptional performance of these networks lies a significant demand for…
Spatial-Division Augmented Occupancy Field for Bone Shape Reconstruction from Biplanar X-Rays
Jixiang Chen, Yiqun Lin, Haoran Sun +1
Retrieving 3D bone anatomy from biplanar X-ray images is crucial since it can significantly reduce radiation exposure compared to traditional CT-based methods. Although various dee…
Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction
Yiqun Lin, Hualiang Wang, Jixiang Chen +1
Cone-Beam Computed Tomography (CBCT) is an indispensable technique in medical imaging, yet the associated radiation exposure raises concerns in clinical practice. To mitigate these…
C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction
Yiqun Lin, Jiewen Yang, Hualiang Wang +3
Cone beam computed tomography (CBCT) is an important imaging technology widely used in medical scenarios, such as diagnosis and preoperative planning. Using fewer projection views…
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models
Tianqi Xiang, Wenjun Yue, Yiqun Lin +3
Performing magnetic resonance imaging (MRI) reconstruction from under-sampled k-space data can accelerate the procedure to acquire MRI scans and reduce patients' discomfort. The re…
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation
Huimin Wu, Xiaomeng Li, Yiqun Lin +1
This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the exist…
Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction
Yiqun Lin, Zhongjin Luo, Wei Zhao +1
Sparse-view cone-beam CT (CBCT) reconstruction is an important direction to reduce radiation dose and benefit clinical applications. Previous voxel-based generation methods represe…
Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels
Wanqin Ma, Huifeng Yao, Yiqun Lin +2
Developing a deep learning method for medical segmentation tasks heavily relies on a large amount of labeled data. However, the annotations require professional knowledge and are l…
Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation
Yiqun Lin, Huifeng Yao, Zezhong Li +2
Segmentation of 3D knee MR images is important for the assessment of osteoarthritis. Like other medical data, the volume-wise labeling of knee MR images is expertise-demanded and t…
RSCFed: Random Sampling Consensus Federated Semi-supervised Learning
Xiaoxiao Liang, Yiqun Lin, Huazhu Fu +2
Federated semi-supervised learning (FSSL) aims to derive a global model by training fully-labeled and fully-unlabeled clients or training partially labeled clients. The existing ap…