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| 001 | 1049900 | ||
| 005 | 20260107202238.0 | ||
| 037 | _ | _ | |a FZJ-2025-05660 |
| 100 | 1 | _ | |a Paul, Richard Dominik |0 P:(DE-Juel1)175101 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a International Conference on Computer Vision, ICCV 2025 |c Honolulu |d 2025-10-19 - 2025-10-23 |w USA |
| 245 | _ | _ | |a How To Make Your Cell Tracker Say "I dunno!" |
| 260 | _ | _ | |c 2025 |
| 295 | 1 | 0 | |a Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) |
| 300 | _ | _ | |a 6914-6923 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1767813071_15420 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 520 | _ | _ | |a Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analysis tools, as the amount of data recorded within a single experiment exceeds what humans are able to overlook. We here propose and benchmark various methods to reason about and quantify uncertainty in linear assignment-based cell tracking algorithms. Our methods take inspiration from statistics and machine learning, leveraging two perspectives on the cell tracking problem explored throughout this work: Considering it as a Bayesian inference problem and as a classification problem. Our methods admit a framework-like character in that they equip any frame-to-frame tracking method with uncertainty quantification. We demonstrate this by applying it to various existing tracking algorithms including the recently presented Transformer-based trackers. We demonstrate empirically that our methods yield useful and well-calibrated tracking uncertainties. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 700 | 1 | _ | |a Seiffarth, Johannes |0 P:(DE-Juel1)176923 |b 1 |u fzj |
| 700 | 1 | _ | |a Rügamer, David |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 3 |u fzj |
| 700 | 1 | _ | |a Nöh, Katharina |0 P:(DE-Juel1)129051 |b 4 |u fzj |
| 856 | 4 | _ | |u https://openaccess.thecvf.com/content/ICCV2025/html/Paul_How_To_Make_Your_Cell_Tracker_Say_I_dunno_ICCV_2025_paper.html |
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| 980 | _ | _ | |a UNRESTRICTED |
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