# Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning

arXiv: https://arxiv.org/abs/2307.09769 · HTML: https://mlsanity.com/paper/2307.09769/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels](https://mlsanity.com/paper/2203.03884/) (2022)
- [Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation](https://mlsanity.com/paper/2002.02255/) (2020)
- [Few-Shot Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.02536/) (2017)
- [Source-Free Domain Adaptation via Distribution Estimation](https://mlsanity.com/paper/2204.11257/) (2022)
- [A Prototype-Oriented Framework for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2110.12024/) (2021)
- [Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss](https://mlsanity.com/paper/1804.10916/) (2018)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling](https://mlsanity.com/paper/2109.09735/) (2021)
- [Source-Free Domain Adaptation for Image Segmentation](https://mlsanity.com/paper/2108.03152/) (2021)
- [Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation](https://mlsanity.com/paper/2101.06853/) (2021)
- [Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation](https://mlsanity.com/paper/2111.12221/) (2021)
- [Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions](https://mlsanity.com/paper/2012.14100/) (2020)
- [Test-Time Adaptation with Shape Moments for Image Segmentation](https://mlsanity.com/paper/2205.07983/) (2022)
- [A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2103.10178/) (2021)
- [Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation](https://mlsanity.com/paper/2203.10332/) (2022)

## Built on by (most cited first, 14 of 14)

- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2603.21309/) (2026)
- [Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting](https://mlsanity.com/paper/2505.08527/) (2025)
- [DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation](https://mlsanity.com/paper/2505.09927/) (2025)
- [FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation](https://mlsanity.com/paper/2406.14308/) (2024)
- [M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2502.17029/) (2025)
- [Unified and Semantically Grounded Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2508.08660/) (2025)
- [HEAL: Learning-Free Source Free Unsupervised Domain Adaptation for Cross-Modality Medical Image Segmentation](https://mlsanity.com/paper/2511.17958/) (2025)
- [Pairwise Similarity Regularization for Semi-supervised Graph Medical Image Segmentation](https://mlsanity.com/paper/2503.12800/) (2025)
- [Tell2Adapt: A Unified Framework for Source Free Unsupervised Domain Adaptation via Vision Foundation Model](https://mlsanity.com/paper/2603.05012/) (2026)
- [From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentation](https://mlsanity.com/paper/2604.17455/) (2026)
- [Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided](https://mlsanity.com/paper/2608.00510/) (2026)
- [Dual Co-Train: Cross-Dataset Ultrasound Tongue Segmentation Under Extreme Data Scarcity](https://mlsanity.com/paper/2608.17983/) (2026)
- [SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation](https://mlsanity.com/paper/2608.22532/) (2026)

## Abstract (arXiv)

Unsupervised domain adaptation (UDA) has increasingly gained interests for its capacity to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. However, typical UDA methods require concurrent access to both the source and target domain data, which largely limits its application in medical scenarios where source data is often unavailable due to privacy concern. To tackle the source data-absent problem, we present a novel two-stage source-free domain adaptation (SFDA) framework for medical image segmentation, where only a well-trained source segmentation model and unlabeled target data are available during domain adaptation. Specifically, in the prototype-anchored feature alignment stage, we first utilize the weights of the pre-trained pixel-wise classifier as source prototypes, which preserve the information of source features. Then, we introduce the bi-directional transport to align the target features with class prototypes by minimizing its expected cost. On top of that, a contrastive learning stage is further devised to utilize those pixels with unreliable predictions for a more compact target feature distribution. Extensive experiments on a cross-modality medical segmentation task demonstrate the superiority of our method in large domain discrepancy settings compared with the state-of-the-art SFDA approaches and even some UDA methods. Code is available at https://github.com/CSCYQJ/MICCAI23-ProtoContra-SFDA.