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<p>Optical imaging techniques, such as hyperspectral imaging combined with machine learning-based analysis, have the potential to revolutionize clinical surgical imaging. However, these modalities face a shortage of large-scale, representative clinical data for training machine learning-based algorithms. While preclinical animal data are abundantly available through standardized experiments and allow for controlled induction of pathological tissue states, it is not ethically possible to obtain similar data from patients. To leverage this situation, we propose ‘xeno-learning’, a cross-species knowledge-transfer concept inspired by xeno-transplantation. Here, using a total of 14,013 hyperspectral 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 such as malperfusion or injection of contrast agent are comparable. Such changes learnt in one species can be transferred to a new species through a ‘physiology-based data augmentation’ method, enabling the large-scale secondary use of preclinical animal data for human application. The resulting benefits promise a high impact of the proposed knowledge-transfer concept on future developments in the field.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月26日

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  • Jan Sellner,
  • Alexander Studier-Fischer,
  • Ahmad Bin Qasim,
  • Silvia Seidlitz,
  • Nicholas Schreck,
  • Minu Tizabi,
  • Manuel Wiesenfarth,
  • Annette Kopp-Schneider,
  • Janne Heinecke,
  • Jule Brandt,
  • Samuel Knoedler,
  • Caelan Max Haney,
  • Gabriel Salg,
  • Berkin Özdemir,
  • Maximilian Dietrich,
  • Maurice Stephan Michel,
  • Felix Nickel,
  • Karl-Friedrich Kowalewski,
  • Lena Maier-Hein

摘要

Optical imaging techniques, such as hyperspectral imaging combined with machine learning-based analysis, have the potential to revolutionize clinical surgical imaging. However, these modalities face a shortage of large-scale, representative clinical data for training machine learning-based algorithms. While preclinical animal data are abundantly available through standardized experiments and allow for controlled induction of pathological tissue states, it is not ethically possible to obtain similar data from patients. To leverage this situation, we propose ‘xeno-learning’, a cross-species knowledge-transfer concept inspired by xeno-transplantation. Here, using a total of 14,013 hyperspectral 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 such as malperfusion or injection of contrast agent are comparable. Such changes learnt in one species can be transferred to a new species through a ‘physiology-based data augmentation’ method, enabling the large-scale secondary use of preclinical animal data for human application. The resulting benefits promise a high impact of the proposed knowledge-transfer concept on future developments in the field.