mlsanityData to 14 Oct 2026

MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

arXiv 2303.09373 ↗First posted 16 Mar 2023cs.CV cs.AI cs.LGcited by 25

Not on the map. Why?

Abstract (arXiv)

Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a well-studied technique that alleviates this label-scarcity problem by leveraging available labels from another domain. In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a unified UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation. To the best of our knowledge, this is the first study that systematically reviews and develops a framework to tackle four different domain shifts in medical image segmentation. More importantly, MAPSeg is the first framework that can be applied to centralized, federated, and test-time UDA while maintaining comparable performance. We compare MAPSeg with previous state-of-the-art methods on a private infant brain MRI dataset and a public cardiac CT-MRI dataset, and MAPSeg outperforms others by a large margin (10.5 Dice improvement on the private MRI dataset and 5.7 on the public CT-MRI dataset). MAPSeg poses great practical value and can be applied to real-world problems. GitHub: https://github.com/XuzheZ/MAPSeg/.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 53 of its 92 references that are in the map. First posted on arXiv 16 Mar 2023.

Builds on

53 of 92 references.

References · 53

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  5. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  6. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  7. 2019Advances and Open Problems in Federated Learningcited by 3,046
  8. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  9. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,428
  10. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,331
  11. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,329
  12. 2021SimMIM: A Simple Framework for Masked Image Modelingcited by 1,153
38 more
  1. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,082
  2. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,066
  3. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  4. 2019Data-Efficient Image Recognition with Contrastive Predictive Codingcited by 673
  5. 2018ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentationcited by 623
  6. 2016Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learningcited by 441
  7. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 422
  8. 2019Confidence Regularized Self-Trainingcited by 419
  9. 2016Unsupervised Domain Adaptation with Residual Transfer Networkscited by 341
  10. 2021DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentationcited by 300
  11. 2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
  12. 2020Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentationcited by 222
  13. 2022Test-Time Training with Masked Autoencoderscited by 212
  14. 2022MIC: Masked Image Consistency for Context-Enhanced Domain Adaptationcited by 178
  15. 2020TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learningcited by 177
  16. 2021Source-Free Domain Adaptation for Semantic Segmentationcited by 168
  17. 2022HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentationcited by 164
  18. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 149
  19. 2020Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentationcited by 134
  20. 2016Multivariate mixture model for myocardium segmentation combining multi-source imagescited by 133
  21. 2022SynthStrip: Skull-Stripping for Any Brain Imagecited by 127
  22. 2019Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentationcited by 121
  23. 2022What to Hide from Your Students: Attention-Guided Masked Image Modelingcited by 118
  24. 2022Revealing the Dark Secrets of Masked Image Modelingcited by 117
  25. 2017No More Discrimination: Cross City Adaptation of Road Scene Segmenterscited by 111
  26. 2020Test-Time Adaptable Neural Networks for Robust Medical Image Segmentationcited by 102
  27. 2020Contextual-Relation Consistent Domain Adaptation for Semantic Segmentationcited by 70
  28. 2021SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classificationcited by 68
  29. 2021Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labelingcited by 61
  30. 2019Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Imagescited by 51
  31. 2022Understanding Masked Image Modeling via Learning Occlusion Invariant Featurecited by 50
  32. 2022On Data Scaling in Masked Image Modelingcited by 46
  33. 2021Federated Multi-Target Domain Adaptationcited by 44
  34. 2021FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learningcited by 43
  35. 2020Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challengecited by 28
  36. 2020Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japancited by 23
  37. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 23
  38. 2021Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentationcited by 20

50 most cited of 53 All 53 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 9; 2026: 11.

25 papers.

Sort:

By area:

  1. 2024Revisiting MAE pre-training for 3D medical image segmentationcited by 35
  2. 2024Masked Image Modeling: A Surveycited by 29
  3. 2024Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignmentcited by 7
  4. 2025BEN: Using Confidence-Guided Matting for Dichotomous Image Segmentationcited by 7
  5. 2024Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Modelscited by 6
  6. 2024Aggregated Attributions for Explanatory Analysis of 3D Segmentation Modelscited by 5
  7. 2025Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentationcited by 3
  8. 2025Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3Dcited by 1
  9. 2025ModalFormer: Multimodal Transformer for Low-Light Image Enhancementcited by 1
  10. 2025Unified and Semantically Grounded Domain Adaptation for Medical Image Segmentationcited by 1
  11. 2025COME: Dual Structure-Semantic Learning with Collaborative MoE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasetscited by 1
  12. 2025TK-Mamba: Marrying KAN With Mamba for Text-Driven 3D Medical Image Segmentationcited by 0
13 more
  1. 2025Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRIcited by 0
  2. 2025Reducing Domain Gap with Diffusion-Based Domain Adaptation for Cell Countingcited by 0
  3. 2026Addressing data annotation scarcity in Brain Tumor Segmentation on 3D MRI scan Using a Semi-Supervised Teacher-Student Frameworkcited by 0
  4. 2026Unsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancycited by 0
  5. 2026SHAPE: Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation for Medical Image Segmentationcited by 0
  6. 2026Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix Itcited by 0
  7. 2026PolarMAE: Efficient Fetal Ultrasound Pre-training via Semantic Screening and Polar-Guided Maskingcited by 0
  8. 2026A Two-Stage Multi-Modal MRI Framework for Lifespan Brain Age Predictioncited by 0
  9. 2026From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentationcited by 0
  10. 2026ASTRA-Net: Anatomy-Specific Transfer and Representation Alignment for Drug-Induced Sleep Endoscopy Segmentationcited by 0
  11. 2026UnDA: Unpaired Domain Alignment for Cross-Modal Knowledge Transfer in Medical Imagingcited by 0
  12. 2026Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guidedcited by 0
  13. 2026Unsupervised Adaptation of 3D CT Foundation Models for 3D CBCT Segmentationcited by 0