| --- |
| library_name: timm |
| license: mit |
| pipeline_tag: image-feature-extraction |
| inference: false |
| 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). |
|
|
