36 citations · 71 across the 10 of their papers we have counts for
10 papers
3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
Xueming Fu, Yingtai Li, Fenghe Tang +4
Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation. Traditional reconstruction techniques often req…
O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis
Jun Li, Kexin Li, Yafeng Zhou +1
Targeted diagnosis and treatment plans for patients with coronary artery disease vary according to atherosclerotic plaque component. Coronary CT angiography (CCTA) is widely used f…
Information-guided pixel augmentation for pixel-wise contrastive learning
Quan Quan, Qingsong Yao, Jun Li +1
Contrastive learning (CL) is a form of self-supervised learning and has been widely used for various tasks. Different from widely studied instance-level contrastive learning, pixel…
Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review
Zikang Xu, Jun Li, Qingsong Yao +3
Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity i…
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Jun Li, Junyu Chen, Yucheng Tang +3
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision. Since medical imaging bear some resemblance…
MixCL: Pixel label matters to contrastive learning
Jun Li, Quan Quan, S. Kevin Zhou
Contrastive learning and self-supervised techniques have gained prevalence in computer vision for the past few years. It is essential for medical image analysis, which is often not…
Universal Segmentation of 33 Anatomies
Pengbo Liu, Yang Deng, Ce Wang +12
In the paper, we present an approach for learning a single model that universally segments 33 anatomical structures, including vertebrae, pelvic bones, and abdominal organs. Our mo…
Which images to label for few-shot medical landmark detection?
Quan Quan, Qingsong Yao, Jun Li +1
The success of deep learning methods relies on the availability of well-labeled large-scale datasets. However, for medical images, annotating such abundant training data often requ…
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Yang Deng, Ce Wang, Yuan Hui +12
Spine-related diseases have high morbidity and cause a huge burden of social cost. Spine imaging is an essential tool for noninvasively visualizing and assessing spinal pathology.…
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Pengbo Liu, Hu Han, Yuanqi Du +9
Purpose: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segme…