# CT Image Harmonization for Enhancing Radiomics Studies

arXiv: https://arxiv.org/abs/2107.01337 · HTML: https://mlsanity.com/paper/2107.01337/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 3 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [STAN-CT: Standardizing CT Image using Generative Adversarial Network](https://mlsanity.com/paper/2004.01307/) (2020)

## Built on by (most cited first, 9 of 9)

- [Cross-Vendor CT Image Data Harmonization Using CVH-CT](https://mlsanity.com/paper/2110.09693/) (2021)
- [Latent Diffusion Model for Medical Image Standardization and Enhancement](https://mlsanity.com/paper/2310.05237/) (2023)
- [DiffusionCT: Latent Diffusion Model for CT Image Standardization](https://mlsanity.com/paper/2301.08815/) (2023)
- [A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging](https://mlsanity.com/paper/2407.13813/) (2024)
- [Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization](https://mlsanity.com/paper/2507.19858/) (2025)
- [Texture-Aware StarGAN for CT data harmonisation](https://mlsanity.com/paper/2503.15058/) (2025)
- [Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction](https://mlsanity.com/paper/2504.10080/) (2025)
- [Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels](https://mlsanity.com/paper/2505.22568/) (2025)
- [RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstruction](https://mlsanity.com/paper/2605.00901/) (2026)

## Abstract (arXiv)

While remarkable advances have been made in Computed Tomography (CT), capturing CT images with non-standardized protocols causes low reproducibility regarding radiomic features, forming a barrier on CT image analysis in a large scale. RadiomicGAN is developed to effectively mitigate the discrepancy caused by using non-standard reconstruction kernels. RadiomicGAN consists of hybrid neural blocks including both pre-trained and trainable layers adopted to learn radiomic feature distributions efficiently. A novel training approach, called Dynamic Window-based Training, has been developed to smoothly transform the pre-trained model to the medical imaging domain. Model performance evaluated using 1401 radiomic features show that RadiomicGAN clearly outperforms the state-of-art image standardization models.