# Color-Quality Invariance for Robust Medical Image Segmentation

arXiv: https://arxiv.org/abs/2502.07200 · HTML: https://mlsanity.com/paper/2502.07200/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2211.14805/) (2022)
- [What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance](https://mlsanity.com/paper/1912.06960/) (2019)
- [Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2306.05254/) (2023)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024)
- [A Practical Framework for Unsupervised Structure Preservation Medical Image Enhancement](https://mlsanity.com/paper/2304.01864/) (2023)

## Abstract (arXiv)

Single-source domain generalization (SDG) in medical image segmentation remains a significant challenge, particularly for images with varying color distributions and qualities. Previous approaches often struggle when models trained on high-quality images fail to generalize to low-quality test images due to these color and quality shifts. In this work, we propose two novel techniques to enhance generalization: dynamic color image normalization (DCIN) module and color-quality generalization (CQG) loss. The DCIN dynamically normalizes the color of test images using two reference image selection strategies. Specifically, the DCIN utilizes a global reference image selection (GRIS), which finds a universal reference image, and a local reference image selection (LRIS), which selects a semantically similar reference image per test sample. Additionally, CQG loss enforces invariance to color and quality variations by ensuring consistent segmentation predictions across transformed image pairs. Experimental results show that our proposals significantly improve segmentation performance over the baseline on two target domain datasets, despite being trained solely on a single source domain. Notably, our model achieved up to a 32.3-point increase in Dice score compared to the baseline, consistently producing robust and usable results even under substantial domain shifts. Our work contributes to the development of more robust medical image segmentation models that generalize across unseen domains. The implementation code is available at https://github.com/RaviShah1/DCIN-CQG.