Record link: 
http://hdl.handle.net/20.500.12708/209922
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Title: 
FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection
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Citation: 
Gutwein, S., Kampel, M., Taschner-Mandl, S., & Licandro, R. (2024). FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection. In C. H. Sudre & R. Mehta (Eds.), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (pp. 23–33). https://doi.org/10.1007/978-3-031-73158-7_3
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Publisher DOI: 
10.1007/978-3-031-73158-7_3
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Gutwein, Simon 
Kampel, Martin 
Taschner-Mandl, Sabine 
Licandro, Roxane 
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Organisational Unit: 
E193-01 - Forschungsbereich Computer Vision 
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML) 
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Published in: 
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging 
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ISBN: 
978-3-031-73158-7
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Volume: 
15167
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Date (published): 
3-Oct-2024
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Event name: 
UNSURE2024 - Uncertainty for Safe Utilization of Machine Learning in Medical Imaging 
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Event date: 
10-Oct-2024
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Event place: 
Marrakesh, Morocco
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Number of Pages: 
11
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Keywords: 
FISH imaging; Contrastive learning; Gene aberration; Uncertainty estimation
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Research Areas: 
Visual Computing and Human-Centered Technology: 100%
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Science Branch: 
1020 - Informatik: 90%
1010 - Mathematik: 10%
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Appears in Collections:
Conference Paper

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