SRPL-SFDA: Sam-Guided Reliable Pseudo-Labels For Source-Free Domain Adaptation in medical image segmentation

Xinya Liu; Jianghao Wu; Tao Lü; Shaoting Zhang; Guotai Wang

doi:10.1016/j.neucom.2025.130749

ScienceGate Book Chapters

JOURNAL ARTICLE

SRPL-SFDA: Sam-Guided Reliable Pseudo-Labels For Source-Free Domain Adaptation in medical image segmentation

Xinya Liu Jianghao Wu Tao Lü Shaoting Zhang Guotai Wang

Year: 2025 Journal: Neurocomputing Vol: 649 Pages: 130749-130749 Publisher: Elsevier BV

DOI: 10.1016/j.neucom.2025.130749

Get Full-Text PDF Get Analytical Report

Keywords:

Artificial intelligence Computer science Segmentation Adaptation (eye) Computer vision Domain adaptation Domain (mathematical analysis) Image (mathematics) Pattern recognition (psychology) Psychology Mathematics Neuroscience

Metrics

Cited By

4.82

FWCI (Field Weighted Citation Impact)

Refs

0.93

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

COVID-19 diagnosis using AI

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Generative Adversarial Networks and Image Synthesis

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

SRPL-SFDA: Sam-Guided Reliable Pseudo-Labels For Source-Free Domain Adaptation in medical image segmentation

Metrics

Citation History

Topics

Related Documents

UPL-SFDA: Uncertainty-Aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

Progressive pseudo-labels enhancement for source-free domain adaptation medical image segmentation

IPLC+: SAM-Guided Iterative Pseudo Label Correction for Source-Free Domain Adaptation in Medical Image Segmentation

RPL-SFDA: Reliable Pseudo Label-Guided Source-Free Cross-Modality Adaptation for NPC GTV Segmentation

IPLC: Iterative Pseudo Label Correction Guided by SAM for Source-Free Domain Adaptation in Medical Image Segmentation