114 citations · 150 across the 16 of their papers we have counts for
16 papers
MindSemantix: Deciphering Brain Visual Experiences with a Brain-Language Model
Ziqi Ren, Jie Li, Xuetong Xue +4
Deciphering the human visual experience through brain activities captured by fMRI represents a compelling and cutting-edge challenge in the field of neuroscience research. Compared…
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation
Zhusi Zhong, Jie Li, Lulu Bi +6
Medical image segmentation based on deep learning often fails when deployed on images from a different domain. The domain adaptation methods aim to solve domain-shift challenges, b…
Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation
Zhenxi Zhang, Ran Ran, Chunna Tian +4
Semi-supervised medical image segmentation offers a promising solution for large-scale medical image analysis by significantly reducing the annotation burden while achieving compar…
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation
Zhenxi Zhang, Ran Ran, Chunna Tian +4
Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abund…
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization
Daniel D Kim, Rajat S Chandra, Jian Peng +14
Deep learning models have demonstrated great potential in medical 3D imaging, but their development is limited by the expensive, large volume of annotated data required. Active lea…
Deep Clustering Survival Machines with Interpretable Expert Distributions
Bojian Hou, Hongming Li, Zhicheng Jiao +3
Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. I…
Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation
Zhenxi Zhang, Chunna Tian, Zhicheng Jiao
Semi-supervised learning methods have been explored in medical image segmentation tasks due to the scarcity of pixel-level annotation in the real scenario. Proto-type alignment bas…
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Ao Luo, Xin Li, Fan Yang +3
In this paper, we study the problem of salient object detection (SOD) for RGB-D images using both color and depth information.A major technical challenge in performing salient obje…
Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction
Zhenxi Zhang, Chunna Tian, Jie Li +3
Medical image segmentation is usually regarded as one of the most important intermediate steps in clinical situations and medical imaging research. Thus, accurately assessing the s…
Hybrid Graph Neural Networks for Crowd Counting
Ao Luo, Fan Yang, Xin Li +4
Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale…
Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates
Yalong Liu, Jie Li, Miaomiao Wang +3
Accurate segmentation of punctate white matter lesions (PWML) in preterm neonates by an automatic algorithm can better assist doctors in diagnosis. However, the existing algorithms…
Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants
Yalong Liu, Jie Li, Ying Wang +5
Accurate segmentation of punctate white matter lesion (PWML) in infantile brains by an automatic algorithm can reduce the potential risk of postnatal development. How to segment PW…
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation
Zhenxi Zhang, Jie Li, Zhusi Zhong +2
3D image segmentation is one of the most important and ubiquitous problems in medical image processing. It provides detailed quantitative analysis for accurate disease diagnosis, a…
Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning
Ziqi Ren, Jie Li, Xuetong Xue +4
Reconstructing visual stimulus (image) only from human brain activity measured with functional Magnetic Resonance Imaging (fMRI) is a significant and meaningful task in Human-AI co…
An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms
Zhusi Zhong, Jie Li, Zhenxi Zhang +2
Cephalometric tracing method is usually used in orthodontic diagnosis and treatment planning. In this paper, we propose a deep learning based framework to automatically detect anat…
Restricting Greed in Training of Generative Adversarial Network
Haoxuan You, Zhicheng Jiao, Haojun Xu +3
Generative adversarial network (GAN) has gotten wide re-search interest in the field of deep learning. Variations of GAN have achieved competitive results on specific tasks. Howeve…