import open_clip
model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Ruler138/CellSenescence-CLIP')
tokenizer = open_clip.get_tokenizer('hf-hub:Ruler138/CellSenescence-CLIP')Cell Senescence CLIP
This gated repository releases two BioCLIP-family checkpoints for research evaluation of cellular senescence images:
- OpenCLIP initialization, 10 epochs:
checkpoints/openclip_init_10epoch_best.pt - Random vision initialization, 10 epochs:
checkpoints/random_vision_10epoch.pt
Both checkpoints use a ViT-L/14 image tower and a BioClinical ModernBERT text tower with a 512-token context. The OpenCLIP model configuration and ModernBERT compatibility shim are included in this repository. Access is gated: request access on Hugging Face and wait for approval before downloading.
Zero-shot evaluation
The results use the lab-provided P4, P12, and DOX image groups. The class meanings follow the accompanying group descriptions: P4 is the non-senescent control, P12 is replicative senescence, and DOX is chemically induced senescence. No model is fine-tuned on these evaluation images. Each image is encoded once; normalized image features are compared with normalized text features for the fixed class descriptions, and the highest cosine similarity determines the prediction.
Two tasks are reported:
- P4 vs P12: binary classification on 100 images, 50 from each group.
- P4 vs P12 vs DOX: three-class classification on all 150 images, 50 from each group.
The same class descriptions are used across models:
| Class | Text description |
|---|---|
| P4 | Fluorescence microscopy of passage-4 adipose-derived mesenchymal stem cells, non-senescent control with elongated spindle-shaped cells and small nuclei. |
| P12 | Fluorescence microscopy of passage-12 adipose-derived mesenchymal stem cells after serial passaging, replicative senescence with enlarged flattened cells and nuclei. |
| DOX | Fluorescence microscopy of adipose-derived mesenchymal stem cells treated with 0.05 micromolar doxorubicin for 7 days, chemically induced senescence with enlarged flattened cells. |
We report accuracy, macro-F1, and recall for each class. The complete two-table comparison, including the evaluated public models, is in evaluation/results/cell_senescence_zero_shot.md; machine-readable values are in the adjacent JSON file.
Results
All 10 listed methods use the same fixed class descriptions. The P4 vs P12 comparison uses 50 images from each group; the three-way comparison uses 50 images from each of P4, P12, and DOX. Metrics are percentages. Bold marks the column maximum. The image-level results describe the supplied cohort; multiple fields from the same well may not be independent biological replicates.
P4 vs P12 (binary)
| Model | Accuracy (%) | Macro-F1 (%) | Recall · P4 control (%) | Recall · P12 senescent (%) |
|---|---|---|---|---|
| MedSigLIP | 50.00 | 33.33 | 100.00 | 0.00 |
| CoCa | 50.00 | 33.33 | 100.00 | 0.00 |
| BioMedCLIP | 55.00 | 48.72 | 20.00 | 90.00 |
| OpenCLIP | 50.00 | 33.33 | 0.00 | 100.00 |
| BMC-LongCLIP | 53.00 | 41.07 | 98.00 | 8.00 |
| PMC-CLIP | 50.00 | 33.33 | 100.00 | 0.00 |
| BMC-CLIP-CF | 50.00 | 48.69 | 66.00 | 34.00 |
| MedPMC-CLIP | 51.00 | 38.56 | 96.00 | 6.00 |
| Ours · random vision · 10 ep | 69.00 | 68.75 | 60.00 | 78.00 |
| Ours · OpenCLIP init · 10 ep | 50.00 | 33.33 | 100.00 | 0.00 |
P4 vs P12 vs DOX (three-class)
| Model | Accuracy (%) | Macro-F1 (%) | Recall · P4 control (%) | Recall · P12 senescent (%) | Recall · DOX senescent (%) |
|---|---|---|---|---|---|
| MedSigLIP | 33.33 | 16.67 | 100.00 | 0.00 | 0.00 |
| CoCa | 40.00 | 31.99 | 46.00 | 0.00 | 74.00 |
| BioMedCLIP | 36.67 | 26.15 | 20.00 | 90.00 | 0.00 |
| OpenCLIP | 46.00 | 37.27 | 0.00 | 70.00 | 68.00 |
| BMC-LongCLIP | 35.33 | 21.60 | 98.00 | 8.00 | 0.00 |
| PMC-CLIP | 31.33 | 17.68 | 92.00 | 0.00 | 2.00 |
| BMC-CLIP-CF | 33.33 | 25.97 | 66.00 | 34.00 | 0.00 |
| MedPMC-CLIP | 34.00 | 20.05 | 96.00 | 6.00 | 0.00 |
| Ours · random vision · 10 ep | 46.00 | 36.72 | 60.00 | 78.00 | 0.00 |
| Ours · OpenCLIP init · 10 ep | 33.33 | 16.67 | 100.00 | 0.00 | 0.00 |
Run the released checkpoints
To run the evaluation, prepare a local data directory with the three image groups shown below. Install the inference dependencies and authenticate to Hugging Face to download a gated checkpoint:
pip install -r evaluation/requirements.txt
hf auth login
The local data root should contain these three folders, each with 50 images:
<data-root>/
├── P4 _ 50 Images/
├── P12 _ 50 Images/
└── DOX _ 50 Images/
Run either released checkpoint; the script reports both requested tasks:
python evaluation/code/evaluate_cell_senescence.py \
--data-root "/path/to/Smaller data sets" \
--checkpoint openclip_init_10epoch_best.pt \
--output openclip_init_results.json
python evaluation/code/evaluate_cell_senescence.py \
--data-root "/path/to/Smaller data sets" \
--checkpoint random_vision_10epoch.pt \
--output random_vision_results.json
The script expects TIFF, PNG, JPEG, or BMP files. It checks that each group contains 50 images and does not crop or resize files beyond the model's standard image preprocessing.
Fine-tuning
This repository provides pretrained checkpoints and evaluation utilities; it does not include training code. For a general reference implementation of CLIP training, see OpenCLIP.
Intended use
For research use only. This model is not a clinical diagnostic device.
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