252 citations · 392 across the 19 of their papers we have counts for
21 papers
Joint Lossless Compression and Steganography for Medical Images via Large Language Models
Pengcheng Zheng, Xiaorong Pu, Kecheng Chen +8
Recently, large language models (LLMs) have driven promising progress in lossless image compression. However, directly adopting existing paradigms for medical images suffers from a…
ChronoSelect: Robust Learning with Noisy Labels via Dynamics Temporal Memory
Jianchao Wang, Qingfeng Li, Pengcheng Zheng +2
Training deep neural networks on real-world datasets is often hampered by the presence of noisy labels, which can be memorized by over-parameterized models, leading to significant…
Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Prior
Pengcheng Zheng, Kecheng Chen, Jiaxin Huang +4
Medical image restoration tasks aim to recover high-quality images from degraded observations, exhibiting emergent desires in many clinical scenarios, such as low-dose CT image den…
Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering
Zichen Wen, Tianyi Wu, Yazhou Ren +4
Multi-view clustering is an important machine learning task for multi-media data, encompassing various domains such as images, videos, and texts. Moreover, with the growing abundan…
Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views
Xinyue Chen, Yazhou Ren, Jie Xu +3
Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Existing approaches often assume th…
Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering
Zichen Wen, Yawen Ling, Yazhou Ren +5
Recently there is a growing focus on graph data, and multi-view graph clustering has become a popular area of research interest. Most of the existing methods are only applicable to…
A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective
Chenhang Cui, Yazhou Ren, Jingyu Pu +5
Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consisten…
Federated Deep Multi-View Clustering with Global Self-Supervision
Xinyue Chen, Jie Xu, Yazhou Ren +5
Federated multi-view clustering has the potential to learn a global clustering model from data distributed across multiple devices. In this setting, label information is unknown an…
Deep Multi-View Subspace Clustering with Anchor Graph
Chenhang Cui, Yazhou Ren, Jingyu Pu +2
Deep multi-view subspace clustering (DMVSC) has recently attracted increasing attention due to its promising performance. However, existing DMVSC methods still have two issues: (1)…
Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition
Zhenqian Wu, Xiaoyuan Li, Yazhou Ren +3
The accuracy of facial expression recognition is typically affected by the following factors: high similarities across different expressions, disturbing factors, and micro-facial m…
Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey
Song Wu, Yazhou Ren, Aodi Yang +5
COVID-19 (Coronavirus disease 2019) has been quickly spreading since its outbreak, impacting financial markets and healthcare systems globally. Countries all around the world have…
Deep Clustering: A Comprehensive Survey
Yazhou Ren, Jingyu Pu, Zhimeng Yang +5
Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clusterin…
Variational Graph Generator for Multi-View Graph Clustering
Jianpeng Chen, Yawen Ling, Jie Xu +6
Multi-view graph clustering (MGC) methods are increasingly being studied due to the explosion of multi-view data with graph structural information. The critical point of MGC is to…
Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs
Zongmo Huang, Yazhou Ren, Xiaorong Pu +1
With the representation learning capability of the deep learning models, deep embedded multi-view clustering (MVC) achieves impressive performance in many scenarios and has become…
EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer
Peng Yi, Kecheng Chen, Zhaoqi Ma +3
Electroencephalogram (EEG) has shown a useful approach to produce a brain-computer interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts (a…
Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering
Jie Xu, Yazhou Ren, Huayi Tang +4
Multi-view clustering, a long-standing and important research problem, focuses on mining complementary information from diverse views. However, existing works often fuse multiple v…
GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising
Kecheng Chen, Jiayu Sun, Jiang Shen +8
Being low-level radiation exposure and less harmful to health, low-dose computed tomography (LDCT) has been widely adopted in the early screening of lung cancer and COVID-19. LDCT…
Non-Linear Fusion for Self-Paced Multi-View Clustering
Zongmo Huang, Yazhou Ren, Xiaorong Pu +1
With the advance of the multi-media and multi-modal data, multi-view clustering (MVC) has drawn increasing attentions recently. In this field, one of the most crucial challenges is…
Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection
Kecheng Chen, Kun Long, Yazhou Ren +2
Deep learning has achieved notable performance in the denoising task of low-quality medical images and the detection task of lesions, respectively. However, existing low-quality me…
Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering
Jie Xu, Yazhou Ren, Huayi Tang +6
Multi-view clustering is an important research topic due to its capability to utilize complementary information from multiple views. However, there are few methods to consider the…
Multi-graph Fusion for Multi-view Spectral Clustering
Zhao Kang, Guoxin Shi, Shudong Huang +4
A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and…