Prost40M is a prostatectomy-specific foundation model pretrained with DINO on a large corpus of H&E prostatectomy slides.
It is designed as a strong feature extractor for computational pathology tasks where subtle prostate-specific morphology matters.

Model At a Glance

Field	Value
Model name	Prost40M
Backbone architecture	`vit_small`
Input size	`224 x 224`
Patch size	`14`
Embedding dimension	`384`
Released weights	Teacher backbone encoder
Domain	H&E prostatectomy histopathology

Quickstart

python

import torch
import timm
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

model = timm.create_model("hf-hub:waticlems/Prost40M", pretrained=True)
model.eval()

transform = create_transform(**resolve_data_config(model.pretrained_cfg, model=model))

img = Image.open("tile.png").convert("RGB")
x = transform(img).unsqueeze(0)
with torch.inference_mode():
    embedding = model(x)  # shape: [1, 384]
print(embedding.shape)

Motivation

Large pathology foundation models are typically trained on broad, multi-organ data. Their generic features transfer well across many settings, but can be less sensitive to fine-grained morphology of a specific organ. Prost40M was developed to evaluate the value of organ-specific pretraining in prostate histopathology.

Training Data

Approx. 40 million image tiles at 0.50 microns per pixel
1888 H&E-stained prostatectomy slides
- 449 slides from 403 patients in the TCGA-PRAD cohort
- 1439 slides from 508 patients in the LEOPARD cohort

Intended Use

Tile-level feature extraction for downstream prostate histopathology tasks

Limitations

Performance can degrade under domain shift (scanner, stain protocol, center)
Learned representations reflect dataset composition and preprocessing choices

License

Apache-2.0

Citation

If you use Prost40M, cite:

@misc{grisi2026bcr,
      title={Deep Learning From Routine Histology Improves Risk Stratification for Biochemical Recurrence in Prostate Cancer}, 
      author={Clément Grisi and Khrystyna Faryna and Nefise Uysal and Vittorio Agosti and Enrico Munari and Solène-Florence Kammerer-Jacquet and Paulo Guilherme de Oliveira Salles and Yuri Tolkach and Reinhard Büttner and Sofiya Semko and Maksym Pikul and Axel Heidenreich and Jeroen van der Laak and Geert Litjens},
      year={2026},
      eprint={2603.14187},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.14187}, 
}