# Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis

arXiv: https://arxiv.org/abs/2410.19789 · HTML: https://mlsanity.com/paper/2410.19789/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [Trends in deep learning for medical hyperspectral image analysis](https://mlsanity.com/paper/2011.13974/) (2020)
- [Video-rate multispectral imaging in laparoscopic surgery: First-in-human application](https://mlsanity.com/paper/2105.13901/) (2021)
- [Semantic segmentation of surgical hyperspectral images under geometric domain shifts](https://mlsanity.com/paper/2303.10972/) (2023)
- [Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis](https://mlsanity.com/paper/2106.08445/) (2021)
- [AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients](https://mlsanity.com/paper/2408.09873/) (2024)
- [Handling Geometric Domain Shifts in Semantic Segmentation of Surgical RGB and Hyperspectral Images](https://mlsanity.com/paper/2408.15373/) (2024)

## Abstract (arXiv)

Novel optical imaging techniques, such as hyperspectral imaging (HSI) combined with machine learning-based (ML) analysis, have the potential to revolutionize clinical surgical imaging. However, these novel modalities face a shortage of large-scale, representative clinical data for training ML algorithms, while preclinical animal data is abundantly available through standardized experiments and allows for controlled induction of pathological tissue states, which is not ethically possible in patients. To leverage this situation, we propose a novel concept called "xeno-learning", a cross-species knowledge transfer paradigm inspired by xeno-transplantation, where organs from a donor species are transplanted into a recipient species. Using a total of 13,874 HSI images from humans as well as porcine and rat models, we show that although spectral signatures of organs differ substantially across species, relative changes resulting from pathologies or surgical manipulation (e.g., malperfusion; injection of contrast agent) are comparable. Such changes learnt in one species can thus be transferred to a new species via a novel "physiology-based data augmentation" method, enabling the large-scale secondary use of preclinical animal data for humans. The resulting ethical, monetary, and performance benefits promise a high impact of the proposed knowledge transfer paradigm on future developments in the field.