# Domain-invariant feature learning in brain MR imaging for content-based image retrieval

arXiv: https://arxiv.org/abs/2501.01326 · HTML: https://mlsanity.com/paper/2501.01326/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Detect and Correct Bias in Multi-Site Neuroimaging Datasets](https://mlsanity.com/paper/2002.05049/) (2020)
- [Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning](https://mlsanity.com/paper/1912.01824/) (2019)
- [Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI](https://mlsanity.com/paper/2108.06518/) (2021)

## Built on by (most cited first, 3 of 3)

- [Feature-level Site Leakage Reduction for Cross-Hospital Chest X-ray Transfer via Self-Supervised Learning](https://mlsanity.com/paper/2604.00263/) (2026)
- [Acquisition of interpretable domain information during brain MR image harmonization for content-based image retrieval](https://mlsanity.com/paper/2510.14535/) (2025)
- [NeuroCBIR: A Fast and Accurate Image Retrieval System for Whole-Brain and Region-Specific MRI](https://mlsanity.com/paper/2610.06502/) (2026)

## Abstract (arXiv)

When conducting large-scale studies that collect brain MR images from multiple facilities, the impact of differences in imaging equipment and protocols at each site cannot be ignored, and this domain gap has become a significant issue in recent years. In this study, we propose a new low-dimensional representation (LDR) acquisition method called style encoder adversarial domain adaptation (SE-ADA) to realize content-based image retrieval (CBIR) of brain MR images. SE-ADA reduces domain differences while preserving pathological features by separating domain-specific information from LDR and minimizing domain differences using adversarial learning. In evaluation experiments comparing SE-ADA with recent domain harmonization methods on eight public brain MR datasets (ADNI1/2/3, OASIS1/2/3/4, PPMI), SE-ADA effectively removed domain information while preserving key aspects of the original brain structure and demonstrated the highest disease search accuracy.