Robust Source-Free Domain Adaptation for Medical Image Segmentation based on Curriculum Learning

• URL: http://arxiv.org/abs/2510.08393v1
• Date: Thu, 09 Oct 2025 16:15:10 GMT
• Title: Robust Source-Free Domain Adaptation for Medical Image Segmentation based on Curriculum Learning
• Authors: Ziqi Zhang, Yuexiang Li, Yawen Huang, Nanjun He, Tao Xu, Liwei Lin, Yefeng Zheng, Shaoxin Li, Feiyue Huang,
• Abstract summary: We propose a curriculum-based framework, namely learning from curriculum (LFC) for source-free domain adaptation.<n>We evaluate the proposed source-free domain adaptation approach on the public cross-domain datasets for fundus segmentation and polyp segmentation.
• Score: 54.514202147709625
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Recent studies have uncovered a new research line, namely source-free domain adaptation, which adapts a model to target domains without using the source data. Such a setting can address the concerns on data privacy and security issues of medical images. However, current source-free domain adaptation frameworks mainly focus on the pseudo label refinement for target data without the consideration of learning procedure. Indeed, a progressive learning process from source to target domain will benefit the knowledge transfer during model adaptation. To this end, we propose a curriculum-based framework, namely learning from curriculum (LFC), for source-free domain adaptation, which consists of easy-to-hard and source-to-target curricula. Concretely, the former curriculum enables the framework to start learning with `easy' samples and gradually tune the optimization direction of model adaption by increasing the sample difficulty. While, the latter can stablize the adaptation process, which ensures smooth transfer of the model from the source domain to the target. We evaluate the proposed source-free domain adaptation approach on the public cross-domain datasets for fundus segmentation and polyp segmentation. The extensive experimental results show that our framework surpasses the existing approaches and achieves a new state-of-the-art.

Related papers

• Out-of-Context Misinformation Detection via Variational Domain-Invariant Learning with Test-Time Training [7.447483980331488]
  Out-of-context misinformation (OOC) is a low-cost form of misinformation in news reports.<n>We propose textbfVDT to enhance the domain adaptation capability for OOC misinformation detection.
  arXiv  Detail & Related papers  (2025-11-13T11:34:26Z)
• DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation [15.107136785491482]
  Domain adaptation addresses the challenge of model performance degradation caused by domain gaps.<n>Access to labeled source domain data, particularly in medical datasets, can be restricted due to privacy policies.<n>We propose a novel source-free domain adaptation (SFDA) framework to address these challenges.
  arXiv  Detail & Related papers  (2025-05-15T03:24:54Z)
• Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence [60.37934652213881]
  Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation. We present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead.
  arXiv  Detail & Related papers  (2024-07-26T17:51:58Z)
• RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation [80.03883315743715]
  Source-free domain adaptation transits the source-trained model towards target domain without exposing the source data. This paradigm is still at risk of data leakage due to adversarial attacks on the source model. We propose a novel approach named RAIN (RegulArization on Input and Network) for Black-Box domain adaptation from both input-level and network-level regularization.
  arXiv  Detail & Related papers  (2022-08-22T18:18:47Z)
• Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling [56.98020855107174]
  Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. In many real-world scenarios, the source data may not be accessible during the model adaptation in the target domain due to privacy issue. We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data.
  arXiv  Detail & Related papers  (2021-09-19T06:38:21Z)
• A Curriculum-style Self-training Approach for Source-Free Semantic Segmentation [91.13472029666312]
  We propose a curriculum-style self-training approach for source-free domain adaptive semantic segmentation. Our method yields state-of-the-art performance on source-free semantic segmentation tasks for both synthetic-to-real and adverse conditions.
  arXiv  Detail & Related papers  (2021-06-22T10:21:39Z)
• Source-Free Open Compound Domain Adaptation in Semantic Segmentation [99.82890571842603]
  In SF-OCDA, only the source pre-trained model and the target data are available to learn the target model. We propose the Cross-Patch Style Swap (CPSS) to diversify samples with various patch styles in the feature-level. Our method produces state-of-the-art results on the C-Driving dataset.
  arXiv  Detail & Related papers  (2021-06-07T08:38:41Z)
• Source-Free Domain Adaptation for Semantic Segmentation [11.722728148523366]
  Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network-based approaches for semantic segmentation heavily rely on the pixel-level annotated data. We propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation.
  arXiv  Detail & Related papers  (2021-03-30T14:14:29Z)
• Universal Source-Free Domain Adaptation [57.37520645827318]
  We propose a novel two-stage learning process for domain adaptation. In the Procurement stage, we aim to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift. In the Deployment stage, the goal is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps.
  arXiv  Detail & Related papers  (2020-04-09T07:26:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.