mlsanityData to 14 Oct 2026

Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised Approach

arXiv 2207.00844 ↗First posted 2 Jul 2022eess.IV cs.CVcited by 8

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Abstract (arXiv)

Medical image synthesis has attracted increasing attention because it could generate missing image data, improving diagnosis and benefits many downstream tasks. However, so far the developed synthesis model is not adaptive to unseen data distribution that presents domain shift, limiting its applicability in clinical routine. This work focuses on exploring domain adaptation (DA) of 3D image-to-image synthesis models. First, we highlight the technical difference in DA between classification, segmentation and synthesis models. Second, we present a novel efficient adaptation approach based on 2D variational autoencoder which approximates 3D distributions. Third, we present empirical studies on the effect of the amount of adaptation data and the key hyper-parameters. Our results show that the proposed approach can significantly improve the synthesis accuracy on unseen domains in a 3D setting. The code is publicly available at https://github.com/WinstonHuTiger/2D_VAE_UDA_for_3D_sythesis

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 9 of its 18 references that are in the map. First posted on arXiv 2 Jul 2022.

Builds on

9 of 18 references.

References · 9

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  2. 2017Adversarial Discriminative Domain Adaptationcited by 1,311
  3. 2016Unsupervised domain adaptation in brain lesion segmentation with adversarial networkscited by 125
  4. 2020Test-Time Adaptable Neural Networks for Robust Medical Image Segmentationcited by 102
  5. 2018Unsupervised domain adaptation for medical imaging segmentation with self-ensemblingcited by 94
  6. 2018Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Losscited by 61
  7. 2019Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentationcited by 33
  8. 2020Continual Learning for Domain Adaptation in Chest X-ray Classificationcited by 31
  9. 2020Modelling the Distribution of 3D Brain MRI using a 2D Slice VAEcited by 20

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 1; 2026: 1.

8 papers.

  1. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  2. 2023The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)cited by 24
  3. 2024Deep Generative Models for 3D Medical Image Synthesiscited by 14
  4. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  5. 2024Memory-efficient High-resolution OCT Volume Synthesis with Cascaded Amortized Latent Diffusion Modelscited by 5
  6. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  7. 2025Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographicscited by 1
  8. 2026VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translationcited by 0