TY - GEN
T1 - Dual Knowledge-Aware Guidance for Source-Free Domain Adaptive Fundus Image Segmentation
AU - Chen, Yu
AU - Wang, Hailing
AU - Wu, Chunwei
AU - Cao, Guitao
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Source-free domain adaptation (SFDA), where only a pre-trained source model is available to adapt to the target domain, has gained widespread application in the medical field. Most existing methods overlook low-quality pseudo-labels, i.e., pseudo-labels with boundary semantic confusion, when learning target domain-specific knowledge, leading to the loss of crucial boundary information. Furthermore, focusing solely on the specific knowledge can drive the model shifts in an uncontrollable direction, resulting in model degradation. To address these issues, we propose Dual Knowledge-aware Guidance (DKG), a novel SFDA method that integrates domain-specific knowledge with domain-invariant knowledge to improve transfer performance. Specifically, the pseudo-label calibration scheme is proposed to reduce semantic bias in high-uncertainty pixels, preserving the boundary information of target domain-specific knowledge. To ensure stable training, we propose a domain-invariant knowledge-based loss strategy, leveraging a confidence-guided mechanism and a consistency constraint. Additionally, we also introduce a dynamic balancing loss to address class imbalance. Extensive experiments on cross-domain fundus image segmentation show that DKG achieves state-of-the-art performance. Code is available at https://github.com/Hanshuqian/DKG
AB - Source-free domain adaptation (SFDA), where only a pre-trained source model is available to adapt to the target domain, has gained widespread application in the medical field. Most existing methods overlook low-quality pseudo-labels, i.e., pseudo-labels with boundary semantic confusion, when learning target domain-specific knowledge, leading to the loss of crucial boundary information. Furthermore, focusing solely on the specific knowledge can drive the model shifts in an uncontrollable direction, resulting in model degradation. To address these issues, we propose Dual Knowledge-aware Guidance (DKG), a novel SFDA method that integrates domain-specific knowledge with domain-invariant knowledge to improve transfer performance. Specifically, the pseudo-label calibration scheme is proposed to reduce semantic bias in high-uncertainty pixels, preserving the boundary information of target domain-specific knowledge. To ensure stable training, we propose a domain-invariant knowledge-based loss strategy, leveraging a confidence-guided mechanism and a consistency constraint. Additionally, we also introduce a dynamic balancing loss to address class imbalance. Extensive experiments on cross-domain fundus image segmentation show that DKG achieves state-of-the-art performance. Code is available at https://github.com/Hanshuqian/DKG
KW - Domain-invariant knowledge
KW - Fundus image
KW - Pseudo-label calibration
KW - Source-free domain adaptation
UR - https://www.scopus.com/pages/publications/105017853496
U2 - 10.1007/978-3-032-04978-0_18
DO - 10.1007/978-3-032-04978-0_18
M3 - 会议稿件
AN - SCOPUS:105017853496
SN - 9783032049773
T3 - Lecture Notes in Computer Science
SP - 183
EP - 193
BT - Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, Proceedings
A2 - Gee, James C.
A2 - Hong, Jaesung
A2 - Sudre, Carole H.
A2 - Golland, Polina
A2 - Alexander, Daniel C.
A2 - Iglesias, Juan Eugenio
A2 - Venkataraman, Archana
A2 - Kim, Jong Hyo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Y2 - 23 September 2025 through 27 September 2025
ER -