24 citations · 54 across the 20 of their papers we have counts for
20 papers
ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic Features
Xin Wei, Yaling Tao, Changde Du +3
Mammography is the primary imaging tool for breast cancer diagnosis. Despite significant strides in applying deep learning to interpret mammography images, efforts that focus predo…
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition
Ming Jin, Danni Zhang, Gangming Zhao +2
Electroencephalography (EEG)-based emotion recognition has gained significant traction due to its accuracy and objectivity. However, the non-stationary nature of EEG signals leads…
Progressive Conservative Adaptation for Evolving Target Domains
Gangming Zhao, Chaoqi Chen, Wenhao He +5
Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. However, in many real-world cases, target data usually emerge seque…
Leveraging Frequency Domain Learning in 3D Vessel Segmentation
Xinyuan Wang, Chengwei Pan, Hongming Dai +4
Coronary microvascular disease constitutes a substantial risk to human health. Employing computer-aided analysis and diagnostic systems, medical professionals can intervene early i…
Identity-Preserving Talking Face Generation with Landmark and Appearance Priors
Weizhi Zhong, Chaowei Fang, Yinqi Cai +4
Generating talking face videos from audio attracts lots of research interest. A few person-specific methods can generate vivid videos but require the target speaker's videos for tr…
Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation
Gangming Zhao, Kongming Liang, Chengwei Pan +4
Vessel segmentation is widely used to help with vascular disease diagnosis. Vessels reconstructed using existing methods are often not sufficiently accurate to meet clinical use st…
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization
Chaoqi Chen, Luyao Tang, Feng Liu +3
Domain generalization (DG) enables generalizing a learning machine from multiple seen source domains to an unseen target one. The general objective of DG methods is to learn semant…
Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization
Lingfeng Li, Huaiwei Cong, Gangming Zhao +3
In recent years, several works have adopted the convolutional neural network (CNN) to diagnose the avascular necrosis of the femoral head (AVNFH) based on X-ray images or magnetic…
Deep 3D Vessel Segmentation based on Cross Transformer Network
Chengwei Pan, Baolian Qi, Gangming Zhao +4
The coronary microvascular disease poses a great threat to human health. Computer-aided analysis/diagnosis systems help physicians intervene in the disease at early stages, where 3…
Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis
Gangming Zhao, Quanlong Feng, Chaoqi Chen +2
During clinical practice, radiologists often use attributes, e.g. morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attribute…
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance
Chengwei Pan, Gangming Zhao, Junjie Fang +6
Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to mu…
Learning Locality and Isotropy in Dialogue Modeling
Han Wu, Haochen Tan, Mingjie Zhan +4
Existing dialogue modeling methods have achieved promising performance on various dialogue tasks with the aid of Transformer and the large-scale pre-trained language models. Howeve…
BOAT: Bilateral Local Attention Vision Transformer
Tan Yu, Gangming Zhao, Ping Li +1
Vision Transformers achieved outstanding performance in many computer vision tasks. Early Vision Transformers such as ViT and DeiT adopt global self-attention, which is computation…
Multi-scale Matching Networks for Semantic Correspondence
Dongyang Zhao, Ziyang Song, Zhenghao Ji +3
Deep features have been proven powerful in building accurate dense semantic correspondences in various previous works. However, the multi-scale and pyramidal hierarchy of convoluti…
MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3D CT Lesions
Penghua Zhai, Huaiwei Cong, Gangming Zhao +4
\emph{Objective and Impact Statement}. With the renaissance of deep learning, automatic diagnostic systems for computed tomography (CT) have achieved many successful applications.…
GraphFPN: Graph Feature Pyramid Network for Object Detection
Gangming Zhao, Weifeng Ge, Yizhou Yu
Feature pyramids have been proven powerful in image understanding tasks that require multi-scale features. State-of-the-art methods for multi-scale feature learning focus on perfor…
GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images
Baolian Qi, Gangming Zhao, Xin Wei +4
Locating diseases in chest X-ray images with few careful annotations saves large human effort. Recent works approached this task with innovative weakly-supervised algorithms such a…
Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning
Gangming Zhao, Baolian Qi, Jinpeng Li
Locating lesions is important in the computer-aided diagnosis of X-ray images. However, box-level annotation is time-consuming and laborious. How to locate lesions accurately with…
Contralaterally Enhanced Networks for Thoracic Disease Detection
Gangming Zhao, Chaowei Fang, Guanbin Li +2
Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapp…
Rethink ReLU to Training Better CNNs
Gangming Zhao, Zhaoxiang Zhang, He Guan +2
Most of convolutional neural networks share the same characteristic: each convolutional layer is followed by a nonlinear activation layer where Rectified Linear Unit (ReLU) is the…