Prost40M
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
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
0.50microns 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},
}