Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Sellner, Jan Studier-Fischer, Alexander Qasim, Ahmad Bin Seidlitz, Silvia Schreck, Nicholas Tizabi, Minu Wiesenfarth, Manuel Kopp-Schneider, Annette Heinecke, Janne Brandt, Jule Knödler, Samuel Haney, Caelan Max Salg, Gabriel Özdemir, Berkin Dietrich, Maximilian Michel, Maurice Stephan Nickel, Felix Kowalewski, Karl-Friedrich Maier-Hein, Lena |
| author_facet | Sellner, Jan Studier-Fischer, Alexander Qasim, Ahmad Bin Seidlitz, Silvia Schreck, Nicholas Tizabi, Minu Wiesenfarth, Manuel Kopp-Schneider, Annette Heinecke, Janne Brandt, Jule Knödler, Samuel Haney, Caelan Max Salg, Gabriel Özdemir, Berkin Dietrich, Maximilian Michel, Maurice Stephan Nickel, Felix Kowalewski, Karl-Friedrich Maier-Hein, Lena |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_19789 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis Sellner, Jan Studier-Fischer, Alexander Qasim, Ahmad Bin Seidlitz, Silvia Schreck, Nicholas Tizabi, Minu Wiesenfarth, Manuel Kopp-Schneider, Annette Heinecke, Janne Brandt, Jule Knödler, Samuel Haney, Caelan Max Salg, Gabriel Özdemir, Berkin Dietrich, Maximilian Michel, Maurice Stephan Nickel, Felix Kowalewski, Karl-Friedrich Maier-Hein, Lena Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.19789 |