318 citations · 583 across the 23 of their papers we have counts for
23 papers
Rethinking Data Perturbation and Model Stabilization for Semi-supervised Medical Image Segmentation
Zhen Zhao, Ye Liu, Meng Zhao +3
Studies on semi-supervised medical image segmentation (SSMIS) have seen fast progress recently. Due to the limited labelled data, SSMIS methods mainly focus on effectively leveragi…
Review of Large Vision Models and Visual Prompt Engineering
Jiaqi Wang, Zhengliang Liu, Lin Zhao +18
Visual prompt engineering is a fundamental technology in the field of visual and image Artificial General Intelligence, serving as a key component for achieving zero-shot capabilit…
Integrated lithium niobate microwave photonic processing engine
Hanke Feng, Tong Ge, Xiaoqing Guo +9
Integrated microwave photonics is an intriguing field that leverages integrated photonic technologies for the generation, transmission, and manipulation of microwave signals in chi…
Artificial General Intelligence for Medical Imaging Analysis
Xiang Li, Lin Zhao, Lu Zhang +16
Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general…
EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention
Xinyu Liu, Houwen Peng, Ningxin Zheng +3
Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them…
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
Zhenxiang Xiao, Yuzhong Chen, Lu Zhang +14
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downst…
Prompt Engineering for Healthcare: Methodologies and Applications
Jiaqi Wang, Enze Shi, Sigang Yu +21
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aim…
Unleash the Potential of Image Branch for Cross-modal 3D Object Detection
Yifan Zhang, Qijian Zhang, Junhui Hou +2
To achieve reliable and precise scene understanding, autonomous vehicles typically incorporate multiple sensing modalities to capitalize on their complementary attributes. However,…
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Jie Liu, Yixiao Zhang, Jie-Neng Chen +7
An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem…
A heterogeneous group CNN for image super-resolution
Chunwei Tian, Yanning Zhang, Wangmeng Zuo +3
Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image super-resolution (SR)…
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
Yifan Zhang, Qijian Zhang, Zhiyu Zhu +2
The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by occlusions, signal missing, or manual annotation errors, can confuse deep 3D object detectors dur…
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels
Xiaoqing Guo, Yixuan Yuan
Noisy labels collected with limited annotation cost prevent medical image segmentation algorithms from learning precise semantic correlations. Previous segmentation arts of learnin…
Image Super-resolution with An Enhanced Group Convolutional Neural Network
Chunwei Tian, Yixuan Yuan, Shichao Zhang +3
CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image supe…
Towards Robust Adaptive Object Detection under Noisy Annotations
Xinyu Liu, Wuyang Li, Qiushi Yang +2
Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate th…
SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection
Wuyang Li, Xinyu Liu, Yixuan Yuan
Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn an object detector generalizing to a novel domain free of annotations. Recent advances align class-condi…
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
Yiming Zeng, Yue Qian, Qijian Zhang +3
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation. We formulate the problem as estimation of point-wise traje…
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis
Chaowei Fang, Liang Wang, Dingwen Zhang +3
Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low intra-slice resolution. Improving the int…
Exploring Gradient Flow Based Saliency for DNN Model Compression
Xinyu Liu, Baopu Li, Zhen Chen +1
Model pruning aims to reduce the deep neural network (DNN) model size or computational overhead. Traditional model pruning methods such as l-1 pruning that evaluates the channel si…
Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
Zhen Chen, Meilu Zhu, Chen Yang +1
Nowadays, deep learning methods with large-scale datasets can produce clinically useful models for computer-aided diagnosis. However, the privacy and ethical concerns are increasin…
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification
Xiaohan Xing, Yuenan Hou, Hang Li +3
The amount of medical images for training deep classification models is typically very scarce, making these deep models prone to overfit the training data. Studies showed that know…
MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation
Xiaoqing Guo, Chen Yang, Baopu Li +1
Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain. Existing self-training based UDA approaches assig…
Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation
Xiaoqing Guo, Zhen Chen, Yixuan Yuan
Automatic melanoma segmentation in dermoscopic images is essential in computer-aided diagnosis of skin cancer. Existing methods may suffer from the hole and shrink problems with li…
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction
Xiaoqing Guo, Chen Yang, Pak Lun Lam +2
Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have import…