2025-11-17 12:36 |
[FZJ-2025-04528]
Preprint
Schiffer, C. ; Boztoprak, Z. ; Kropp, J.-O. ; et al
CytoNet: A Foundation Model for the Human Cerebral Cortex
To study how the human brain works, we need to explore the organization of the cerebral cortex and its detailed cellular architecture. We introduce CytoNet, a foundation model that encodes high-resolution microscopic image patches of the cerebral cortex into highly expressive feature representations, enabling comprehensive brain analyses. [...]
OpenAccess: PDF;
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2025-11-12 11:21 |
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2025-10-28 12:51 |
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2025-10-28 12:50 |
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2025-10-27 13:26 |
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2025-10-27 07:03 |
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2025-10-24 11:38 |
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2025-10-20 12:24 |
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2025-10-20 12:20 |
[FZJ-2025-04215]
Preprint
Seiffarth, J. ; Nöh, K.
PyUAT: Open-source Python framework for efficient and scalable cell tracking
Tracking individual cells in live-cell imaging provides fundamental insights, inevitable for studying causes and consequences of phenotypic heterogeneity, responses to changing environmental conditions or stressors. Microbial cell tracking, characterized by stochastic cell movements and frequent cell divisions, remains a challenging task when imaging frame rates must be limited to avoid counterfactual results. [...]
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2025-10-20 12:06 |
[FZJ-2025-04214]
Preprint
Paul, R. D. ; Seiffarth, J. ; Rügamer, D. ; et al
How To Make Your Cell Tracker Say 'I dunno!'
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. [...]
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