8 citations · 9 across the 21 of their papers we have counts for
6 papers · 1 filter
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…
LGM-Pose: A Lightweight Global Modeling Network for Real-time Human Pose Estimation
Biao Guo, Cong Zhou, Fangmin Guo +3
Most of the current top-down multi-person pose estimation lightweight methods are based on multi-branch parallel pure CNN network architecture, which often struggle to capture the…
VISTA: Mitigating Semantic Inertia in Video-LLMs via Training-Free Dynamic Chain-of-Thought Routing
Hongbo Jin, Jiayu Ding, Siyi Xie +2
Recent advancements in Large Language Models have successfully transitioned towards System 2 reasoning, yet applying these paradigms to video understanding remains challenging. Whi…
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…