Abstract
Employing self-supervised learning (SSL) methodologies as-
sumes par-amount significance in handling unlabeled polyp datasets
when building deep learning-based automatic polyp segmentation mod-
els. However, the intricate privacy dynamics surrounding medical data
often preclude seamless data sharing among disparate medical centers.
Federated learning (FL) emerges as a formidable solution to this privacy
conundrum, yet within the realm of FL, optimizing model generaliza-
tion stands as a pressing imperative. Robust generalization capabilities
are imperative to ensure the model’s efficacy across diverse geographi-
cal domains post-training on localized client datasets. In this paper, a
Federated self-supervised Domain Generalization method is proposed
to enhance the generalization capacity of federated and Label-efficient
intestinal polyp segmentation, named LFDG. Based on a classical SSL
method, DropPos, LFDG proposes an adversarial learning-based data
augmentation method (SSADA) to enhance the data diversity. LFDG
further proposes a relaxation module based on Source-reconstruction and
Augmentation-masking (SRAM) to maintain stability in feature learning.
We have validated LFDG on polyp images from six medical centers. The
performance of our method achieves 3.80% and 3.92% better than the
baseline and other recent FL methods and SSL methods, respectively.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/ADSMI2024_096.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{TanXin_Federated_MICCAISAT2026,
author = { Tan, Xinyi AND Wang, Jiacheng AND Wang, Liansheng},
title = { { Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17276},
month = {pending},
page = {pending}
}