282 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN9 papers
- Jinan UniversityCN9 papers
- National Astronomical ObservatoriesCN8 papers
- Peking UniversityCN7 papers
- The University of Texas Southwestern Medical CenterUS6 papers
- Kavli Institute for Theoretical SciencesCN5 papers
- Brera Astronomical ObservatoryIT3 papers
- Institute of Space SciencesES3 papers
- Nanjing University of Science and TechnologyCN3 papers
- Osservatorio Astronomico di PadovaIT3 papers
- South China University of TechnologyCN3 papers
- University of California San DiegoUS3 papers
15 papers · 1 filter
Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction
Weikai Qu, Sijun Liang, Xianfeng Li +7
In computed tomography imaging, metal implants frequently generate severe artifacts that compromise image quality and hinder diagnostic accuracy. There are three main challenges in…
3D Distance-color-coded Assessment of PCI Stent Apposition via Deep-learning-based Three-dimensional Multi-object Segmentation
Xiaoyang Qin, Hao Huang, Shuaichen Lin +8
Coronary artery disease poses a significant global health challenge, often necessitating percutaneous coronary intervention (PCI) with stent implantation. Assessing stent appositio…
Don't Fear Peculiar Activation Functions: EUAF and Beyond
Qianchao Wang, Shijun Zhang, Dong Zeng +4
In this paper, we propose a new super-expressive activation function called the Parametric Elementary Universal Activation Function (PEUAF). We demonstrate the effectiveness of PEU…
Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids
Junchen Liu, Wenbo Hu, Zhuo Yang +6
Despite significant advancements in Neural Radiance Fields (NeRFs), the renderings may still suffer from aliasing and blurring artifacts, since it remains a fundamental challenge t…
CLIP in Medical Imaging: A Survey
Zihao Zhao, Yuxiao Liu, Han Wu +8
Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training paradigm, successfully introduces text supervision to vision models. It has shown promising resu…
CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation
Shengcong Chen, Changxing Ding, Minfeng Liu +2
Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate…
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
Jun Ma, Yao Zhang, Song Gu +14
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved…
Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
Yujia Zhou, Shumao Pang, Jun Cheng +7
Deformation field estimation is an important and challenging issue in many medical image registration applications. In recent years, deep learning technique has become a promising…
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation
Chenhong Zhou, Changxing Ding, Xinchao Wang +2
Class imbalance has emerged as one of the major challenges for medical image segmentation. The model cascade (MC) strategy significantly alleviates the class imbalance issue via ru…
MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation
Jiawei Zhang, Yuzhen Jin, Jilan Xu +2
Biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) an…
A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation
Yujiao Tang, Feng Yang, Shaofeng Yuan +1
The computer-aided diagnosis (CAD) systems can highly improve the reliability and efficiency of melanoma recognition. As a crucial step of CAD, skin lesion segmentation has the uns…
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
Shulong Li, Panpan Xu, Bin Li +8
To predict lung nodule malignancy with a high sensitivity and specificity, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the ou…
Radon Inversion via Deep Learning
Ji He, Jianhua Ma
Radon transform is widely used in physical and life sciences and one of its major applications is the X-ray computed tomography (X-ray CT), which is significant in modern health ex…
Automatic multi-objective based feature selection for classification
Zhiguo Zhou, Shulong Li, Genggeng Qin +3
Objective: Accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate…
Direct Automated Quantitative Measurement of Spine via Cascade Amplifier Regression Network
Shumao Pang, Stephanie Leung, Ilanit Ben Nachum +2
Automated quantitative measurement of the spine (i.e., multiple indices estimation of heights, widths, areas, and so on for the vertebral body and disc) is of the utmost importance…