---
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).