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Solving the domain shift problem during inference is essential in medical imaging as most deep-learning based solutions suffer from it. In practice, domain shifts are tackled by performing Unsupervised Domain Adaptation (UDA), where a model is adapted to an unlabeled target domain by leveraging the labelled source domain. In medical scenarios, the data comes with huge privacy concerns making it difficult to apply standard UDA techniques. Hence, a closer clinical setting is Source-Free UDA (SFUDA), where we have access to source trained model but not the source data during adaptation. Methods trying to solve SFUDA typically address the domain shift using pseudo-label based self-training techniques. However due to domain shift, these pseudo-labels are usually of high entropy and denoising them still does not make them perfect labels to supervise the model. Therefore, adapting the source model with noisy pseudo labels reduces its segmentation capability while addressing the domain shift. To this end, we propose a two-stage approach for source-free domain adaptive image segmentation: 1) Target-specific adaptation followed by 2) Task-specific adaptation. In the Stage-I, we focus on learning target-specific representation and generating high-quality pseudo labels by leveraging a proposed ensemble entropy minimization loss and selective voting strategy. In Stage II, we focus on improving segmentation performance by utilizing teacher-student self-training and augmentation-guided consistency loss, leveraging the pseudo labels obtained from Stage I. We evaluate our proposed method on both 2D fundus datasets and 3D MRI volumes across 7 different domain shifts where we achieve better performance than recent UDA and SF-UDA methods for medical image segmentation. Code is available at https://github.com/Vibashan/tt-sfuda.
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This content will become publicly available on April 11, 2026
Supportive Negatives Spectral Augmentation for Source-Free Cross-Domain Segmentation
Source-free domain adaptation (SFDA) aims to transfer knowledge from the well-trained source model and optimize it to adapt target data distribution. SFDA methods are suitable for medical image segmentation task due to its data-privacy protection and achieve promising performances. However, cross-domain distribution shift makes it difficult for the adapted model to provide accurate decisions on several hard instances and negatively affects model generalization. To overcome this limitation, a novel method `supportive negatives spectral augmentation' (SNSA) is presented in this work. Concretely, SNSA includes the instance selection mechanism to automatically discover a few hard samples for which source model produces incorrect predictions. And, active learning strategy is adopted to re-calibrate their predictive masks. Moreover, SNSA deploys the spectral augmentation between hard instances and others to encourage source model to gradually capture and adapt the attributions of target distribution. Considerable experimental studies demonstrate that annotating merely 4%~5% of negative instances from the target domain significantly improves segmentation performance over previous methods.
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- Award ID(s):
- 2528483
- PAR ID:
- 10609452
- Publisher / Repository:
- AAAI Press
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 39
- Issue:
- 10
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 10573 to 10581
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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