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Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction

arXiv 2504.10080 ↗First posted 14 Apr 2025cs.CV eess.IVcited by 0

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Abstract (arXiv)

In this paper, we explore how conventional image enhancement can improve model robustness in medical image analysis. By applying commonly used normalization methods to images from various vendors and studying their influence on model generalization in transfer learning, we show that the nonlinear characteristics of domain-specific image dynamics cannot be addressed by simple linear transforms. To tackle this issue, we reformulate the image harmonization task as an exposure correction problem and propose a method termed Global Deep Curve Estimation (GDCE) to reduce domain-specific exposure mismatch. GDCE performs enhancement via a pre-defined polynomial function and is trained with a "domain discriminator", aiming to improve model transparency in downstream tasks compared to existing black-box methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 36 references that are in the map. First posted on arXiv 14 Apr 2025.

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21 of 36 references.

References · 21

  1. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  2. 2020Zero-Reference Deep Curve Estimation for Low-Light Image Enhancementcited by 542
  3. 2018Generating Adversarial Examples with Adversarial Networkscited by 258
  4. 2017Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?cited by 221
  5. 2016Synthesizing the preferred inputs for neurons in neural networks via deep generator networkscited by 181
  6. 2017HDR image reconstruction from a single exposure using deep CNNscited by 167
  7. 2020Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipelinecited by 132
  8. 2018ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Contentcited by 89
  9. 2021Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methodscited by 83
  10. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  11. 2019Dual Illumination Estimation for Robust Exposure Correctioncited by 30
  12. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
9 more
  1. 2023When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlationscited by 18
  2. 2020STAN-CT: Standardizing CT Image using Generative Adversarial Networkcited by 11
  3. 2024Counterfactual contrastive learning: robust representations via causal image synthesiscited by 11
  4. 2024Source Matters: Source Dataset Impact on Model Robustness in Medical Imagingcited by 10
  5. 2021CT Image Harmonization for Enhancing Radiomics Studiescited by 9
  6. 2021Cross-Vendor CT Image Data Harmonization Using CVH-CTcited by 8
  7. 2023Latent Diffusion Model for Medical Image Standardization and Enhancementcited by 6
  8. 2020Breast mass detection in digital mammography based on anchor-free architecturecited by 4
  9. 2024Exploring connections of spectral analysis and transfer learning in medical imagingcited by 1

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