# Unsupervised domain adaptation for medical imaging segmentation with self-ensembling

arXiv: https://arxiv.org/abs/1811.06042 · HTML: https://mlsanity.com/paper/1811.06042/

## Key facts

- Cited by 94 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 30 of 94)

- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [A Fine-Grained Analysis on Distribution Shift](https://mlsanity.com/paper/2110.11328/) (2021)
- [Self-supervised Augmentation Consistency for Adapting Semantic Segmentation](https://mlsanity.com/paper/2105.00097/) (2021)
- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [Context-Aware Mixup for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2108.03557/) (2021)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling](https://mlsanity.com/paper/2109.09735/) (2021)
- [Domain Adaptation for Time Series Under Feature and Label Shifts](https://mlsanity.com/paper/2302.03133/) (2023)
- [CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2201.02831/) (2022)
- [Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation](https://mlsanity.com/paper/2303.14360/) (2023)
- [Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2310.11320/) (2023)
- [On-the-Fly Test-time Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2203.05574/) (2022)
- [An Empirical Framework for Domain Generalization in Clinical Settings](https://mlsanity.com/paper/2103.11163/) (2021)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation](https://mlsanity.com/paper/2111.12221/) (2021)
- [ManyDG: Many-domain Generalization for Healthcare Applications](https://mlsanity.com/paper/2301.08834/) (2023)
- [Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey](https://mlsanity.com/paper/2112.03241/) (2021)
- [Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2109.08852/) (2021)
- [Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration](https://mlsanity.com/paper/2209.05160/) (2022)
- [Unsupervised Domain Adaptation for Semantic Segmentation with Pseudo Label Self-Refinement](https://mlsanity.com/paper/2310.16979/) (2023)
- [Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification](https://mlsanity.com/paper/2103.09097/) (2021)
- [Challenges for machine learning in clinical translation of big data imaging studies](https://mlsanity.com/paper/2107.05630/) (2021)
- [Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2112.04665/) (2021)
- [On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging](https://mlsanity.com/paper/2309.01488/) (2023)
- [Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization](https://mlsanity.com/paper/2103.04813/) (2021)
- [Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning](https://mlsanity.com/paper/2109.05664/) (2021)
- [Target and Task specific Source-Free Domain Adaptive Image Segmentation](https://mlsanity.com/paper/2203.15792/) (2022)
- [Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2111.11629/) (2021)
- [A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation](https://mlsanity.com/paper/2204.02779/) (2022)
- [CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentation](https://mlsanity.com/paper/2212.14121/) (2022)
- [Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training](https://mlsanity.com/paper/2307.16660/) (2023)

## Abstract (arXiv)

Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabelled data.