Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis Article Swipe
Jan Sellner
,
Alexander Studier‐Fischer
,
Ahmad Bin Qasim
,
Silvia Seidlitz
,
Nicholas Schreck
,
Minu D. Tizabi
,
Manuel Wiesenfarth
,
Annette Kopp‐Schneider
,
Samuel Knödler
,
Caelán Max Haney
,
Gabriel Alexander Salg
,
Berkin Özdemir
,
Maximilian Dietrich
,
Maurice Stephan Michel
,
Felix Nickel
,
Karl‐Friedrich Kowalewski
,
Lena Maier‐Hein
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.19789
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.19789
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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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.19789
- https://arxiv.org/pdf/2410.19789
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404313201
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404313201Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.19789Digital Object Identifier
- Title
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Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysisWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-10-15Full publication date if available
- Authors
-
Jan Sellner, Alexander Studier‐Fischer, Ahmad Bin Qasim, Silvia Seidlitz, Nicholas Schreck, Minu D. Tizabi, Manuel Wiesenfarth, Annette Kopp‐Schneider, Samuel Knödler, Caelán Max Haney, Gabriel Alexander Salg, Berkin Özdemir, Maximilian Dietrich, Maurice Stephan Michel, Felix Nickel, Karl‐Friedrich Kowalewski, Lena Maier‐HeinList of authors in order
- Landing page
-
https://arxiv.org/abs/2410.19789Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2410.19789Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2410.19789Direct OA link when available
- Concepts
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Transfer of learning, Spectral analysis, Computer science, Artificial intelligence, Image (mathematics), Deep learning, Pattern recognition (psychology), Physics, Astronomy, SpectroscopyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.agent) | 137 |
| abstract_inverted_index.allows | 50 |
| abstract_inverted_index.animal | 41, 168 |
| abstract_inverted_index.called | 74 |
| abstract_inverted_index.differ | 120 |
| abstract_inverted_index.field. | 194 |
| abstract_inverted_index.future | 190 |
| abstract_inverted_index.humans | 104 |
| abstract_inverted_index.images | 102 |
| abstract_inverted_index.impact | 182 |
| abstract_inverted_index.learnt | 142 |
| abstract_inverted_index.organs | 85, 119 |
| abstract_inverted_index.tissue | 56 |
| abstract_inverted_index.changes | 125, 141 |
| abstract_inverted_index.concept | 73 |
| abstract_inverted_index.humans. | 171 |
| abstract_inverted_index.imaging | 2, 7 |
| abstract_inverted_index.machine | 11 |
| abstract_inverted_index.method, | 160 |
| abstract_inverted_index.models, | 111 |
| abstract_inverted_index.optical | 1 |
| abstract_inverted_index.porcine | 108 |
| abstract_inverted_index.promise | 179 |
| abstract_inverted_index.propose | 70 |
| abstract_inverted_index.species | 89, 145, 153 |
| abstract_inverted_index.states, | 57 |
| abstract_inverted_index.through | 46 |
| abstract_inverted_index.However, | 23 |
| abstract_inverted_index.although | 115 |
| abstract_inverted_index.benefits | 178 |
| abstract_inverted_index.clinical | 20, 33 |
| abstract_inverted_index.combined | 9 |
| abstract_inverted_index.contrast | 136 |
| abstract_inverted_index.enabling | 161 |
| abstract_inverted_index.ethical, | 174 |
| abstract_inverted_index.imaging. | 22 |
| abstract_inverted_index.inspired | 81 |
| abstract_inverted_index.leverage | 66 |
| abstract_inverted_index.paradigm | 80, 188 |
| abstract_inverted_index.possible | 62 |
| abstract_inverted_index.proposed | 185 |
| abstract_inverted_index.relative | 124 |
| abstract_inverted_index.shortage | 29 |
| abstract_inverted_index.species, | 123 |
| abstract_inverted_index.species. | 95 |
| abstract_inverted_index.spectral | 116 |
| abstract_inverted_index.surgical | 21, 130 |
| abstract_inverted_index.training | 36 |
| abstract_inverted_index.transfer | 79, 187 |
| abstract_inverted_index.analysis, | 14 |
| abstract_inverted_index.available | 45 |
| abstract_inverted_index.ethically | 61 |
| abstract_inverted_index.induction | 53 |
| abstract_inverted_index.injection | 134 |
| abstract_inverted_index.knowledge | 78, 186 |
| abstract_inverted_index.monetary, | 175 |
| abstract_inverted_index.patients. | 64 |
| abstract_inverted_index.potential | 17 |
| abstract_inverted_index.recipient | 94 |
| abstract_inverted_index.resulting | 126, 173 |
| abstract_inverted_index.secondary | 164 |
| abstract_inverted_index.abundantly | 44 |
| abstract_inverted_index.controlled | 52 |
| abstract_inverted_index.modalities | 26 |
| abstract_inverted_index.signatures | 117 |
| abstract_inverted_index.situation, | 68 |
| abstract_inverted_index.algorithms, | 38 |
| abstract_inverted_index.comparable. | 139 |
| abstract_inverted_index.experiments | 48 |
| abstract_inverted_index.large-scale | 163 |
| abstract_inverted_index.pathologies | 128 |
| abstract_inverted_index.performance | 177 |
| abstract_inverted_index.preclinical | 40, 167 |
| abstract_inverted_index.techniques, | 3 |
| abstract_inverted_index.transferred | 149 |
| abstract_inverted_index.developments | 191 |
| abstract_inverted_index.large-scale, | 31 |
| abstract_inverted_index.manipulation | 131 |
| abstract_inverted_index.pathological | 55 |
| abstract_inverted_index.standardized | 47 |
| abstract_inverted_index.transplanted | 91 |
| abstract_inverted_index.augmentation" | 159 |
| abstract_inverted_index.cross-species | 77 |
| abstract_inverted_index.hyperspectral | 6 |
| abstract_inverted_index.malperfusion; | 133 |
| abstract_inverted_index.revolutionize | 19 |
| abstract_inverted_index.substantially | 121 |
| abstract_inverted_index.learning-based | 12 |
| abstract_inverted_index.representative | 32 |
| abstract_inverted_index."xeno-learning", | 75 |
| abstract_inverted_index."physiology-based | 157 |
| abstract_inverted_index.xeno-transplantation, | 83 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 17 |
| citation_normalized_percentile |