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Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis
Computer Vision and Pattern Recognition · Submitted 15 Oct 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Novel optical imaging techniques, such as hyperspectral imaging (HSI) combined with machine learning-based (ML) analysis, have the potential to revolutionize clinical surgical imaging. | background 1.00 | 0.04 | 0.03 | hedged possibility 1.00 |
| s1 | 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. | gap 0.64 | 0.09 | 0.03 | asserted 1.00 |
| s2 | 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. | contribution 0.99 | 0.43 | 0.08 | asserted 0.92 |
| s3 | 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. | result 0.99 | 0.96 | 0.30 | asserted 1.00 |
| s4 | 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. | method 0.53 | 0.73 | 0.50 | asserted 0.86 |
| s5 | The resulting ethical, monetary, and performance benefits promise a high impact of the proposed knowledge transfer paradigm on future developments in the field. | interpretation 0.98 | 0.14 | 0.41 | hedged possibility 0.80 |
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Original abstract
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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Uses the method or tool of 3 papers
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Finally, the organ transplantation method proposed in [ref] followed by a L1 re-normalization of the image was applied.
Handling Geometric Domain Shifts in Semantic Segmentation of Surgical RGB and Hyperspectral Images · Segmentation models · 0.93
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During the last 10 epochs, stochastic weight averaging was applied [ref] .
Averaging Weights Leads to Wider Optima and Better Generalization · Segmentation models · 1.00
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Adam [ref] was used as an optimization algorithm with an exponential learning rate scheme (initial learning rate: $η=0.001$, decay rate $γ=0.99$, Adam decay rates $β_1=0.9$and $β_2=0.999$).
Adam: A Method for Stochastic Optimization · Segmentation models · 1.00
Compares with 1 paper
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It should be noticed that the intra-species performance values (dsc: <ref>; nsd <ref>) are consistent with the state-of-the-art literature [ref] .
Handling Geometric Domain Shifts in Semantic Segmentation of Surgical RGB and Hyperspectral Images · Segmentation networks fail to generalize across species · 0.90
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