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library_name: timm
license: mit
pipeline_tag: image-feature-extraction
inference: false # the widget cannot feed 5-channel TIFFs
tags:
- microscopy
- single-cell
- self-supervised
- dinov2
- vision-transformer
- biology
---
# scdino-v2-base
Self-supervised Vision Transformer (DINOv2) for 5-channel
single-cell microscopy crops of human peripheral blood mononuclear cells (PBMCs). It returns one
128-dimensional embedding per crop.
- **Input:** 50×50 px crops with 5 channels (H, W, C), in this order:
| # | Channel | Markers |
|---|---|---|
| 1 | 647 nm | CD3 (APC), CD14 (Alexa Fluor 647) |
| 2 | Brightfield | – |
| 3 | DAPI | – |
| 4 | 488 nm | CD4, CD19 (FITC) |
| 5 | 594 nm | CD8, CD16, CD56 (PE), CD20 (Alexa Fluor 594) |
- **Data:** A labelled set of more than 1.3 million single-cell images of seven immune cell types is openly available: [Morphologically annotated single-cell images of human PBMCs](https://doi.org/10.3929/ethz-c-000806089).
## Usage
See the README of the code repository, https://github.com/JonasMeirer/scDINOv2, for how to load the model,
preprocess crops and evaluate embeddings.
Usage on images from other microscopes, stains, cells or crop sizes might need new normalization statistics and retraining.
**Not a diagnostic tool, and not for clinical use.**
## Citation
The publication describing this model is currently under review. This section
will be updated with the reference once it is published.
## License
MIT, like the scDINO code. Training used timm (Apache-2.0) and code vendored from Lightly (MIT).
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