mlsanityData to 7 Oct 2026

Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation

arXiv 2311.12437 ↗First posted 21 Nov 2023cs.CVcited by 3

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Abstract (arXiv)

Unsupervised cross-modality domain adaptation is a challenging task in medical image analysis, and it becomes more challenging when source and target domain data are collected from multiple institutions. In this paper, we present our solution to tackle the multi-institutional unsupervised domain adaptation for the crossMoDA 2023 challenge. First, we perform unpaired image translation to translate the source domain images to the target domain, where we design a dynamic network to generate synthetic target domain images with controllable, site-specific styles. Afterwards, we train a segmentation model using the synthetic images and further reduce the domain gap by self-training. Our solution achieved the 1st place during both the validation and testing phases of the challenge. The code repository is publicly available at https://github.com/MedICL-VU/crossmoda2023.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 21 Nov 2023.

Builds on

22 of 34 references.

References · 22

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  3. 2020Contrastive Learning for Unpaired Image-to-Image Translationcited by 637
  4. 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
  5. 2016A Learned Representation For Artistic Stylecited by 340
  6. 2021ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentationcited by 184
  7. 2020Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translationcited by 60
  8. 2022CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentationcited by 58
  9. 2022QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translationcited by 39
  10. 2022ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalitiescited by 22
  11. 2022COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Trainingcited by 20
  12. 2021Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Trainingcited by 16
10 more
  1. 2023COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-trainingcited by 16
  2. 2023Cross-modal tumor segmentation using generative blending augmentation and self trainingcited by 6
  3. 2021Unsupervised Cross-Modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model Ensemblecited by 5
  4. 2022Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusioncited by 5
  5. 2022Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learningcited by 5
  6. 2023Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentationcited by 5
  7. 2022Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannomacited by 4
  8. 2022Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentationcited by 4
  9. 2023MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentationcited by 4
  10. 2023MAP: Domain Generalization via Meta-Learning on Anatomy-Consistent Pseudo-Modalitiescited by 3

Built on by

Cited by year (2026 to 7 Oct)

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

3 papers.

  1. 2025crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023cited by 1
  2. 2024Predicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challengecited by 0
  3. 2026IntraStyler: Intra-Domain Style Synthesis for Cross-Modality MRI Domain Adaptationcited by 0