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--- |
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license: apache-2.0 |
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library_name: timm |
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tags: |
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- histopathology |
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- pathology |
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- dino |
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- vision-transformer |
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- prostate |
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- feature-extraction |
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pipeline_tag: image-feature-extraction |
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--- |
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# Prost40M |
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**Prost40M** is a prostatectomy-specific foundation model pretrained with DINO on a large corpus of H&E prostatectomy slides. |
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It is designed as a strong feature extractor for computational pathology tasks where subtle prostate-specific morphology matters. |
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## Model At a Glance |
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| Field | Value | |
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| --- | --- | |
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| Model name | Prost40M | |
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| Backbone architecture | `vit_small` | |
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| Input size | `224 x 224` | |
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| Patch size | `14` | |
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| Embedding dimension | `384` | |
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| Released weights | Teacher backbone encoder | |
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| Domain | H&E prostatectomy histopathology | |
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## Quickstart |
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```python |
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import torch |
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import timm |
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from PIL import Image |
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from timm.data import resolve_data_config |
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from timm.data.transforms_factory import create_transform |
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model = timm.create_model("hf-hub:waticlems/Prost40M", pretrained=True) |
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model.eval() |
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transform = create_transform(**resolve_data_config(model.pretrained_cfg, model=model)) |
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img = Image.open("tile.png").convert("RGB") |
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x = transform(img).unsqueeze(0) |
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with torch.inference_mode(): |
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embedding = model(x) # shape: [1, 384] |
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print(embedding.shape) |
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``` |
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## Motivation |
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Large pathology foundation models are typically trained on broad, multi-organ |
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data. Their generic features transfer well across many settings, but can be less |
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sensitive to fine-grained morphology of a specific organ. Prost40M was developed |
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to evaluate the value of organ-specific pretraining in prostate histopathology. |
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## Training Data |
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- Approx. 40 million image tiles at `0.50` microns per pixel |
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- 1888 H&E-stained prostatectomy slides |
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- 449 slides from 403 patients in the TCGA-PRAD cohort |
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- 1439 slides from 508 patients in the LEOPARD cohort |
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## Intended Use |
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- Tile-level feature extraction for downstream prostate histopathology tasks |
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## Limitations |
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- Performance can degrade under domain shift (scanner, stain protocol, center) |
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- Learned representations reflect dataset composition and preprocessing choices |
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## License |
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Apache-2.0 |
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## Citation |
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If you use **Prost40M**, cite: |
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- _citation to be added soon_ |
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