# Experience with Single Domain Generalization in Real World Medical Imaging Deployments

arXiv: https://arxiv.org/abs/2601.16359 · HTML: https://mlsanity.com/paper/2601.16359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 22 Jan 2026.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [CLIP the Gap: A Single Domain Generalization Approach for Object Detection](https://mlsanity.com/paper/2301.05499/) (2023)
- [OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression](https://mlsanity.com/paper/2206.02338/) (2022)
- [Self-Distilled Vision Transformer for Domain Generalization](https://mlsanity.com/paper/2207.12392/) (2022)
- [ERM++: An Improved Baseline for Domain Generalization](https://mlsanity.com/paper/2304.01973/) (2023)
- [CLIP-DR: Textual Knowledge-Guided Diabetic Retinopathy Grading with Ranking-aware Prompting](https://mlsanity.com/paper/2407.04068/) (2024)
- [EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition](https://mlsanity.com/paper/2304.01508/) (2023)
- [DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability](https://mlsanity.com/paper/2308.10453/) (2023)
- [STORM: Strategic Orchestration of Modalities for Rare Event Classification](https://mlsanity.com/paper/2412.02805/) (2024)
- [Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLM](https://mlsanity.com/paper/2501.16481/) (2025)

## Abstract (arXiv)

A desirable property of any deployed artificial intelligence is generalization across domains, i.e. data generation distribution under a specific acquisition condition. In medical imagining applications the most coveted property for effective deployment is Single Domain Generalization (SDG), which addresses the challenge of training a model on a single domain to ensure it generalizes well to unseen target domains. In multi-center studies, differences in scanners and imaging protocols introduce domain shifts that exacerbate variability in rare class characteristics. This paper presents our experience on SDG in real life deployment for two exemplary medical imaging case studies on seizure onset zone detection using fMRI data, and stress electrocardiogram based coronary artery detection. Utilizing the commonly used application of diabetic retinopathy, we first demonstrate that state-of-the-art SDG techniques fail to achieve generalized performance across data domains. We then develop a generic expert knowledge integrated deep learning technique DL+EKE and instantiate it for the DR application and show that DL+EKE outperforms SOTA SDG methods on DR. We then deploy instances of DL+EKE technique on the two real world examples of stress ECG and resting state (rs)-fMRI and discuss issues faced with SDG techniques.