6 papers · 1 filter
Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation
Xingyue Zhao, Wenke Huang, Linghao Zhuang +11
Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains challenging. Exis…
Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
Xingyue Zhao, Wenke Huang, Linghao Zhuang +7
Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of segmentation foundation models for medical imaging. However, most federated LoRA methods adopt a uniform aggre…
Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints
Lijun Guo, Haoyu Zhao, Xingyue Zhao +5
Building high-fidelity digital twins of articulated objects from visual data remains a central challenge. Existing approaches depend on multi-view captures of the object in discret…
Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation
Xingyue Zhao, Wenke Huang, Xingguang Wang +5
Federated learning enables multiple medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains a major…
pFedSAM: Personalized Federated Learning of Segment Anything Model for Medical Image Segmentation
Tong Wang, Xingyue Zhao, Linghao Zhuang +5
Medical image segmentation is crucial for computer-aided diagnosis, yet privacy constraints hinder data sharing across institutions. Federated learning addresses this limitation, b…
ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification
Linghao Zhuang, Ying Zhang, Gege Yuan +2
Precise classification of megakaryocytes is crucial for diagnosing myelodysplastic syndromes. Although self-supervised learning has shown promise in medical image analysis, its app…