# ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training

arXiv: https://arxiv.org/abs/2206.02288 · HTML: https://mlsanity.com/paper/2206.02288/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 5 Jun 2022.

## Builds on (most cited first, 22 of 22)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Semi-supervised Domain Adaptation via Minimax Entropy](https://mlsanity.com/paper/1904.06487/) (2019)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data](https://mlsanity.com/paper/2010.03622/) (2020)
- [Deep Co-Training for Semi-Supervised Image Recognition](https://mlsanity.com/paper/1803.05984/) (2018)
- [Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/1901.08211/) (2019)
- [Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation](https://mlsanity.com/paper/2006.16806/) (2020)
- [Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2007.09375/) (2020)
- [Supervised Uncertainty Quantification for Segmentation with Multiple Annotations](https://mlsanity.com/paper/1907.01949/) (2019)
- [Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation](https://mlsanity.com/paper/2101.06853/) (2021)
- [Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation](https://mlsanity.com/paper/2106.12497/) (2021)
- [Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation](https://mlsanity.com/paper/2004.00794/) (2020)
- [Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation](https://mlsanity.com/paper/2103.04705/) (2021)
- [Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models](https://mlsanity.com/paper/1911.07421/) (2019)
- [Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation](https://mlsanity.com/paper/2108.12545/) (2021)
- [Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis](https://mlsanity.com/paper/2106.12499/) (2021)
- [Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference](https://mlsanity.com/paper/2107.10931/) (2021)
- [Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation](https://mlsanity.com/paper/2107.13467/) (2021)
- [Self-semantic contour adaptation for cross modality brain tumor segmentation](https://mlsanity.com/paper/2201.05022/) (2022)

## Built on by (most cited first, 20 of 20)

- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2310.11320/) (2023)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI](https://mlsanity.com/paper/2305.19404/) (2023)
- [Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning](https://mlsanity.com/paper/2307.02798/) (2023)
- [Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification](https://mlsanity.com/paper/2308.05770/) (2023)
- [An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation](https://mlsanity.com/paper/2407.02893/) (2024)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation](https://mlsanity.com/paper/2309.06075/) (2023)
- [Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation](https://mlsanity.com/paper/2504.09601/) (2025)
- [Exploring Generalizable Distillation for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2207.12995/) (2022)
- [Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI](https://mlsanity.com/paper/2402.06982/) (2024)
- [S&D Messenger: Exchanging Semantic and Domain Knowledge for Generic Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.07763/) (2024)
- [M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2502.17029/) (2025)
- [Dual-supervised Asymmetric Co-training for Semi-supervised Medical Domain Generalization](https://mlsanity.com/paper/2509.20785/) (2025)
- [Multi-Domain Brain Vessel Segmentation Through Feature Disentanglement](https://mlsanity.com/paper/2510.00665/) (2025)
- [An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation](https://mlsanity.com/paper/2605.21835/) (2026)
- [Diverse Teaching and Label Propagation for Generic Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2508.08549/) (2025)
- [SegMix:Shuffle-based Feedback Learning for Semantic Segmentation of Pathology Images](https://mlsanity.com/paper/2604.15777/) (2026)

## Abstract (arXiv)

Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts between source and target domains, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain. Recent literature, however, has indicated that the performance is still far from satisfactory in the presence of significant domain shifts. Nonetheless, delineating a few target samples is usually manageable and particularly worthwhile, due to the substantial performance gain. Inspired by this, we aim to develop semi-supervised domain adaptation (SSDA) for medical image segmentation, which is largely underexplored. We, thus, propose to exploit both labeled source and target domain data, in addition to unlabeled target data in a unified manner. Specifically, we present a novel asymmetric co-training (ACT) framework to integrate these subsets and avoid the domination of the source domain data. Following a divide-and-conquer strategy, we explicitly decouple the label supervisions in SSDA into two asymmetric sub-tasks, including semi-supervised learning (SSL) and UDA, and leverage different knowledge from two segmentors to take into account the distinction between the source and target label supervisions. The knowledge learned in the two modules is then adaptively integrated with ACT, by iteratively teaching each other, based on the confidence-aware pseudo-label. In addition, pseudo label noise is well-controlled with an exponential MixUp decay scheme for smooth propagation. Experiments on cross-modality brain tumor MRI segmentation tasks using the BraTS18 database showed, even with limited labeled target samples, ACT yielded marked improvements over UDA and state-of-the-art SSDA methods and approached an "upper bound" of supervised joint training.