# Domain Shift in Computer Vision models for MRI data analysis: An Overview

arXiv: https://arxiv.org/abs/2010.07222 · HTML: https://mlsanity.com/paper/2010.07222/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 16 of 16)

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- [Medical Image Segmentation with Domain Adaptation: A Survey](https://mlsanity.com/paper/2311.01702/) (2023)
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- [Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification](https://mlsanity.com/paper/2309.05794/) (2023)
- [Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation](https://mlsanity.com/paper/2406.02125/) (2024)
- [Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study](https://mlsanity.com/paper/2503.22862/) (2025)
- [Negligible effect of brain MRI data preprocessing for tumor segmentation](https://mlsanity.com/paper/2204.05278/) (2022)
- [Revisiting Hidden Representations in Transfer Learning for Medical Imaging](https://mlsanity.com/paper/2302.08272/) (2023)
- [CPATTA: Conformal Supervision Allocation For Active Test-Time Adaptation](https://mlsanity.com/paper/2509.25692/) (2025)
- [Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation](https://mlsanity.com/paper/2507.17971/) (2025)
- [Data-Efficient Meningioma Segmentation via Implicit Spatiotemporal Mixing and Sim2Real Semantic Injection](https://mlsanity.com/paper/2601.17031/) (2026)
- [Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosis](https://mlsanity.com/paper/2604.05649/) (2026)
- [AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis](https://mlsanity.com/paper/2604.17846/) (2026)
- [A Systematic Benchmark of Intensity Normalisation Methods for 3D Knee MRI Segmentation and Cross-Domain Generalisability](https://mlsanity.com/paper/2607.20028/) (2026)

## Abstract (arXiv)

Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yetonly a few applications are now in clinical use and one of the reasons for that is poor transferability of themodels to data from different sources or acquisition domains. Development of new methods and algorithms forthe transfer of training and adaptation of the domain in multi-modal medical imaging data is crucial for thedevelopment of accurate models and their use in clinics. In present work, we overview methods used to tackle thedomain shift problem in machine learning and computer vision. The algorithms discussed in this survey includeadvanced data processing, model architecture enhancing and featured training, as well as predicting in domaininvariant latent space. The application of the autoencoding neural networks and their domain-invariant variationsare heavily discussed in a survey. We observe the latest methods applied to the magnetic resonance imaging(MRI) data analysis and conclude on their performance as well as propose directions for further research.