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| Hauptseite > Publikationsdatenbank > CytoNet: A Foundation Model for the Human Cerebral Cortex - Applications in BigBrain and Beyond > print |
| Hauptseite > Publikationsdatenbank > CytoNet: A Foundation Model for the Human Cerebral Cortex - Applications in BigBrain and Beyond > print |
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| 100 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 9th BigBrain Workshop - HIBALL Closing Symposium |c Berlin |d 2025-10-27 - 2025-10-29 |w Germany |
| 245 | _ | _ | |a CytoNet: A Foundation Model for the Human Cerebral Cortex - Applications in BigBrain and Beyond |
| 260 | _ | _ | |c 2025 |
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| 520 | _ | _ | |a CytoNet: A Foundation Model for the Human Cerebral Cortex - Applications in BigBrain and Beyond 28 Oct 2025, 12:00 15m Langenbeck-Virchow-HausTalk Session 1: Multiscale Data Integration & AI-based ProcessingSpeaker Christian Schiffer (Forschungszentrum Jülich)DescriptionMicroscopic analysis of cytoarchitecture in the human cerebral cortex is essential for understanding the anatomical basis of brain function. We present CytoNet, a foundation model that encodes high-resolution microscopic image patches into expressive feature representations suitable for whole-brain analysis. CytoNet leverages the spatial relationship between anatomical proximity and microstructural similarity to learn biologically meaningful features using self-supervised learning, without the need for manual annotations. The learned features are consistent across regions and subjects, can be computed at arbitrarily dense sampling locations, and support a wide range of neuroscientific analyses. We demonstrate state-of-the-art performance for brain area classification, cortical layer segmentation, morphological parameter estimation, and unsupervised parcellation. As a foundation model, CytoNet provides a unified representation of cortical microarchitecture and establishes a basis for comprehensive analyses of cytoarchitecture and its relationship to other structural and functional principles at the whole-brain level. |
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