Cover Image

pFedSAM: Personalized Federated Learning of Segment Anything Model for Medical Image Segmentation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Tong, Zhao, Xingyue, Zhuang, Linghao, Zhao, Haoyu, Yin, Jiayi, He, Yuyang, Yu, Gang, Lin, Bo
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation
by: Zhao, Xingyue, et al.
Published: (2025)

SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation
by: Nasirihaghighi, Sahar, et al.
Published: (2025)

Segment Anything in Medical Images
by: Ma, Jun, et al.
Published: (2023)

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
by: Wu, Junde, et al.
Published: (2023)

I-MedSAM: Implicit Medical Image Segmentation with Segment Anything
by: Wei, Xiaobao, et al.
Published: (2023)

DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation
by: Gao, Yifan, et al.
Published: (2023)

MedSAM2: Segment Anything in 3D Medical Images and Videos
by: Ma, Jun, et al.
Published: (2025)

Continual Alignment for SAM: Rethinking Foundation Models for Medical Image Segmentation in Continual Learning
by: Wang, Jiayi, et al.
Published: (2025)

S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation
by: Paranjape, Jay N., et al.
Published: (2024)

SAM3D: Segment Anything Model in Volumetric Medical Images
by: Bui, Nhat-Tan, et al.
Published: (2023)

Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation
by: Ye, Maoyuan, et al.
Published: (2024)

ClassWise-SAM-Adapter: Parameter Efficient Fine-tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation
by: Pu, Xinyang, et al.
Published: (2024)

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation
by: Lin, Li, et al.
Published: (2024)

SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation
by: Chen, Xinrun, et al.
Published: (2023)

RobustSAM: Segment Anything Robustly on Degraded Images
by: Chen, Wei-Ting, et al.
Published: (2024)

Region-Guided Attack on the Segment Anything Model (SAM)
by: Liu, Xiaoliang, et al.
Published: (2024)

SAM 2: Segment Anything in Images and Videos
by: Ravi, Nikhila, et al.
Published: (2024)

X-SAM: From Segment Anything to Any Segmentation
by: Wang, Hao, et al.
Published: (2025)

SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation
by: Chen, Tianrun, et al.
Published: (2025)

SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation
by: Xiong, Xinyu, et al.
Published: (2024)

MedSAM3: Delving into Segment Anything with Medical Concepts
by: Liu, Anglin, et al.
Published: (2025)

Biomedical SAM 2: Segment Anything in Biomedical Images and Videos
by: Yan, Zhiling, et al.
Published: (2024)

SAM3-I: Segment Anything with Instructions
by: Li, Jingjing, et al.
Published: (2025)

DarkSAM: Fooling Segment Anything Model to Segment Nothing
by: Zhou, Ziqi, et al.
Published: (2024)

SAM 3: Segment Anything with Concepts
by: Carion, Nicolas, et al.
Published: (2025)

SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
by: Wang, Guoan, et al.
Published: (2024)

SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation
by: Tang, Zixuan, et al.
Published: (2026)

Segment Anything Model for Medical Images?
by: Huang, Yuhao, et al.
Published: (2023)

SAM Audio: Segment Anything in Audio
by: Shi, Bowen, et al.
Published: (2025)

SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More
by: Chen, Tianrun, et al.
Published: (2024)

Medical SAM 2: Segment medical images as video via Segment Anything Model 2
by: Zhu, Jiayuan, et al.
Published: (2024)

SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model
by: Chan, Trevor J., et al.
Published: (2024)

SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation
by: Huo, Jiayu, et al.
Published: (2024)

FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
by: Xiang, Yangyang, et al.
Published: (2024)

Segment Anything in Medical Images and Videos: Benchmark and Deployment
by: Ma, Jun, et al.
Published: (2024)

Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
by: Wei, Zhikai, et al.
Published: (2024)

Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X-ray Images
by: de Carvalho, Gabriel Bellon, et al.
Published: (2024)

UV-SAM: Adapting Segment Anything Model for Urban Village Identification
by: Zhang, Xin, et al.
Published: (2024)

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain
by: Yoon, Hangyul, et al.
Published: (2024)

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer
by: Shao, Xinyuan, et al.
Published: (2024)