# HistoEncoder: A digital pathology foundation model for prostate cancer

Article · Pohjonen J, Batouche O, Kantola J, Rannikko A, Sandeman K, Erickson A, Pitkänen E, Mirtti T

- Archive id: doi:10.1016/j.jpi.2026.100715
- Published: 2026-08-31
- Venue: Journal of pathology informatics
- DOI: https://doi.org/10.1016/j.jpi.2026.100715
- PMID: 42859633
- Citations: 0
- Source URL: https://doi.org/10.1016/j.jpi.2026.100715
- Provenance: europepmc

## Abstract

Foundation models are trained on massive amounts of data to capture complex patterns in images. Subsequently, a wide range of downstream tasks can be adopted with minimal computational resources. We have developed HistoEncoder, a foundation model for prostate cancer digital pathology by pre-training on 48 million prostate tissue tile images. HistoEncoder allows automated extraction of histological features highly predictive of Gleason patterns achieving comparable performance with substantially larger pan-cancer foundation models while being much more efficient. By fine-tuning the model with a small amount of data and computational resources, we describe two clinical use cases for HistoEncoder. First, HistoEncoder can be used to automatically annotate large-scale datasets with high accuracy. Second, we show that HistoEncoder-derived histology clusters contain prognostic information of a similar magnitude to Gleason grading in internal cross-validation. Lightweight foundation models such as HistoEncoder allow organizations to build effective clinical software tools without the need for extensive datasets and heavy computing.

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [AI in Clinical Medicine](https://space-kitty.com/topics/ai-medicine)

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