Record link: 
http://hdl.handle.net/20.500.12708/208727
-
Title: 
On the Importance of Local and Global Feature Learning for Automated Measurable Residual Disease Detection in Flow Cytometry Data
en
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
-
Publisher DOI: 
10.1007/978-3-031-78198-8_21
-
Publication Type: 
Inproceedings - Full-Paper Contribution
en
Language: 
English
-
Authors: 
Weijler, Lisa 
Reiter, Michael 
Hermosilla, Pedro 
Maurer-Granofszky, Margarita 
Dworzak, Michael 
-
Organisational Unit: 
E193-01 - Forschungsbereich Computer Vision 
-
Published in: 
Pattern Recognition 
-
ISBN: 
978-3-031-78198-8
-
Volume: 
15312
-
Date (published): 
2025
-
Event name: 
27th International Conference on Pattern Recognition (ICPR 2024) 
en
Event date: 
1-Dec-2024 - 5-Dec-2024
-
Event place: 
Kolkata, India
-
Number of Pages: 
16
-
Peer reviewed: 
Yes
-
Keywords: 
Flow Cytometry; Automated MRD Detection; Deep Learning; Self-Attention; Graph Neural Networks
en
Project title: 
MyeFLOW- Automated minimal residual disease assessment in childhood acute myeloid leukemia: 2883041 (Wirtschaftsagentur Wien Ein Fonds der Stadt Wien) 
-
Research Areas: 
Visual Computing and Human-Centered Technology: 100%
-
Science Branch: 
1020 - Informatik: 90%
1010 - Mathematik: 10%
-
Appears in Collections:
Conference Paper

Show full item record

Page view(s)

7
checked on Jan 14, 2025

Google ScholarTM

Check