mlsanityData to 14 Oct 2026

Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows

arXiv 2301.11551 ↗First posted 27 Jan 2023cs.CVcited by 5

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Abstract (arXiv)

In this paper, we propose an unsupervised framework based on normalizing flows that harmonizes MR images to mimic the distribution of the source domain. The proposed framework consists of three steps. First, a shallow harmonizer network is trained to recover images of the source domain from their augmented versions. A normalizing flow network is then trained to learn the distribution of the source domain. Finally, at test time, a harmonizer network is modified so that the output images match the source domain's distribution learned by the normalizing flow model. Our unsupervised, source-free and task-independent approach is evaluated on cross-domain brain MRI segmentation using data from four different sites. Results demonstrate its superior performance compared to existing methods.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 18 of its 35 references that are in the map. First posted on arXiv 27 Jan 2023.

Builds on

18 of 35 references.

References · 18

  1. 2016Density estimation using Real NVPcited by 2,042
  2. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
  3. 2020Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptationcited by 848
  4. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  5. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 280
  6. 2019Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Designcited by 262
  7. 2020Why Normalizing Flows Fail to Detect Out-of-Distribution Datacited by 221
  8. 2022Parameter-free Online Test-time Adaptationcited by 189
  9. 2020MoFlow: An Invertible Flow Model for Generating Molecular Graphscited by 185
  10. 2019Noise Flow: Noise Modeling with Conditional Normalizing Flowscited by 108
  11. 2020Test-Time Adaptable Neural Networks for Robust Medical Image Segmentationcited by 102
  12. 2021Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformationcited by 68
6 more
  1. 2019AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flowscited by 33
  2. 20163D fully convolutional networks for subcortical segmentation in MRI: A large-scale studycited by 19
  3. 2021Information-based Disentangled Representation Learning for Unsupervised MR Harmonizationcited by 16
  4. 2021Harmonization with Flow-based Causal Inferencecited by 14
  5. 2020Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignmentcited by 10
  6. 2020Realistic Image Normalization for Multi-Domain Segmentationcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 0; 2026: 1.

5 papers.

  1. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  2. 2024Unpaired Volumetric Harmonization of Brain MRI with Conditional Latent Diffusioncited by 5
  3. 2024Harmonizing Flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonizationcited by 4
  4. 2024ContourDiff: Unpaired Medical Image Translation with Structural Consistencycited by 2
  5. 2026Unified Multi-Site Multi-Sequence Brain MRI Harmonization Enriched by Biomedical Semantic Stylecited by 0