跨物种迁移学习，让动物实验数据助力人类医学影像分析
Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis
- 基于生理变化相似性，从动物数据中迁移病理特征到人类
- 在13,874张光谱图像上验证跨物种知识转移有效，提升模型性能
- 适合临床数据稀缺的医学影像研究者，尤其关注动物实验转化
新型光学成像技术如高光谱成像（HSI）结合机器学习分析，有望革新临床手术影像。然而，这类技术面临大规模代表性临床数据不足的问题，而标准化实验下动物数据丰富，可控制诱导病理状态，这在患者中无法实现。为此，我们提出“异种学习”（xeno-learning）概念，类比器官异种移植，利用13,874张人类及猪、大鼠模型的HSI图像，发现尽管不同物种间器官光谱特征差异显著，但病理或手术操作（如灌注不良、对比剂注射）引起的相对变化具有可比性。通过一种新型“基于生理的数据增强”方法，可将某一物种学到的变化特征迁移到新物种，实现预临床动物数据的大规模二次利用。该方法带来伦理、成本与性能三方面优势，对未来发展具有重要影响。
原文摘要 · Abstract (English)
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.
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