8 citations · 8 across the 12 of their papers we have counts for
13 papers
AgentFactory: Towards Automated Agentic System Design and Optimization
Enci Zhang, Haofeng Wang, Yuesheng Zhu +2
Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and complex task execution. Howev…
SPOTR: Spatio-temporal Pooling One-Token Reconstruction for Universal Physiological Signal Self-supervised Learning
Yiyu Gui, Mingzhi Chen, Yuesheng Zhu +2
Physiological signals such as EEG, ECG, and PPG are widely used in clinical monitoring. Recent self-supervised learning (SSL) methods offer an attractive way to leverage unlabeled…
DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models
Hanwen Zhang, Qiaojin Shen, Yuxi Liu +2
Foundation Models (FMs) have demonstrated strong generalization across diverse vision tasks. However, their deployment in federated settings is hindered by high computational deman…
Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences
Shudong Liu, Hanwen Zhang, Xiuling Wang +2
One-shot federated learning (OSFL) reduces the communication cost and privacy risks of iterative federated learning by constructing a global model with a single round of communicat…
A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation
Yufei Ma, Hanwen Zhang, Qiya Yang +2
In multi-center scenarios, One-Shot Federated Learning (OSFL) has attracted increasing attention due to its low communication overhead, requiring only a single round of transmissio…
FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective
Zhekai Zhou, Shudong Liu, Zhaokun Zhou +4
Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a shared model without sharing their local private data. Howeve…
MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis
Jianhao Xie, Ziang Zhang, Zhenyu Weng +2
Recent advancements in deep learning for medical image segmentation are often limited by the scarcity of high-quality training data.While diffusion models provide a potential solut…
Federated Learning for Medical Image Classification: A Comprehensive Benchmark
Zhekai Zhou, Guibo Luo, Mingzhi Chen +2
The federated learning paradigm is wellsuited for the field of medical image analysis, as it can effectively cope with machine learning on isolated multicenter data while protectin…
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models
Mingzhi Chen, Yiyu Gui, Yuqi Su +3
Electroencephalogram (EEG) classification has been widely used in various medical and engineering applications, where it is important for understanding brain function, diagnosing d…
ProMamba: Prompt-Mamba for polyp segmentation
Jianhao Xie, Ruofan Liao, Ziang Zhang +3
Detecting polyps through colonoscopy is an important task in medical image segmentation, which provides significant assistance and reference value for clinical surgery. However, ac…
MPCPA: Multi-Center Privacy Computing with Predictions Aggregation based on Denoising Diffusion Probabilistic Model
Guibo Luo, Hanwen Zhang, Xiuling Wang +2
Privacy-preserving computing is crucial for multi-center machine learning in many applications such as healthcare and finance. In this paper a Multi-center Privacy Computing framew…
A Segmentation Foundation Model for Diverse-type Tumors
Jianhao Xie, Ziang Zhang, Guibo Luo +1
Large pre-trained models with their numerous model parameters and extensive training datasets have shown excellent performance in various tasks. Many publicly available medical ima…
FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation
Yuxi Liu, Guibo Luo, Yuesheng Zhu
Medical image segmentation is crucial for clinical diagnosis. The Segmentation Anything Model (SAM) serves as a powerful foundation model for visual segmentation and can be adapted…