Gast ::
| Hauptseite > Publikationsdatenbank > CytoNet: A Foundation Model for the Human Cerebral Cortex |
| Hauptseite > Publikationsdatenbank > CytoNet: A Foundation Model for the Human Cerebral Cortex |
| Preprint | FZJ-2025-04528 |
; ; ; ; ; ; ;
2025
arXiv
This record in other databases:
Please use a persistent id in citations: doi:10.48550/ARXIV.2511.01870 doi:10.34734/FZJ-2025-04528
Abstract: 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. CytoNet employs self-supervised learning using spatial proximity as a powerful training signal, without requiring manual labelling. The resulting features are anatomically sound and biologically relevant. They encode general aspects of cortical architecture and unique brain-specific traits. We demonstrate top-tier performance in tasks such as cortical area classification, cortical layer segmentation, cell morphology estimation, and unsupervised brain region mapping. As a foundation model, CytoNet offers a consistent framework for studying cortical microarchitecture, supporting analyses of its relationship with other structural and functional brain features, and paving the way for diverse neuroscientific investigations.
Keyword(s): Neurons and Cognition (q-bio.NC) ; Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG) ; FOS: Biological sciences ; FOS: Computer and information sciences ; I.2.6; I.2.10; I.4.7; I.5.1; I.5.4
|
The record appears in these collections: |
PDF