Record link: 
http://hdl.handle.net/20.500.12708/208727
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Title: 
On the Importance of Local and Global Feature Learning for Automated Measurable Residual Disease Detection in Flow Cytometry Data
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Citation: 
Weijler, L., Reiter, M., Hermosilla, P., Maurer-Granofszky, M., & Dworzak, M. (2025). On the Importance of Local and Global Feature Learning for Automated Measurable Residual Disease Detection in Flow Cytometry Data. In A. Antonacopoulos, S. Chaudhuri, R. Chellappa, Cl. Liu, S. Bhattacharya, & U. Pal (Eds.), Pattern Recognition (pp. 316–331). https://doi.org/10.1007/978-3-031-78198-8_21
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Publisher DOI: 
10.1007/978-3-031-78198-8_21
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Weijler, Lisa 
Reiter, Michael 
Hermosilla, Pedro 
Maurer-Granofszky, Margarita 
Dworzak, Michael 
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Organisational Unit: 
E193-01 - Forschungsbereich Computer Vision 
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Published in: 
Pattern Recognition 
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ISBN: 
978-3-031-78198-8
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Volume: 
15312
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Date (published): 
2025
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Event name: 
27th International Conference on Pattern Recognition (ICPR 2024) 
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Event date: 
1-Dec-2024 - 5-Dec-2024
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Event place: 
Kolkata, India
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Number of Pages: 
16
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Peer reviewed: 
Yes
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Keywords: 
Flow Cytometry; Automated MRD Detection; Deep Learning; Self-Attention; Graph Neural Networks
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Project title: 
MyeFLOW- Automated minimal residual disease assessment in childhood acute myeloid leukemia: 2883041 (Wirtschaftsagentur Wien Ein Fonds der Stadt Wien) 
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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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