our foundation model

The industry leading foundation model by pathologists for pathologists

"RudolfV: A Foundation Model by Pathologists for Pathologists"

Jonas Dippel, Barbara Feulner, Tobias Winterhoff, Simon Schallenberg, Gabriel Dernbach, Andreas Kunft, Stephan Tietz, Philipp Jurmeister, David Horst, Lukas Ruff, Klaus-Robert Müller, Frederick Klauschen, Maximilian Alber

Our histopathology foundation model is developed using a “pathologist led” approach that maximizes model performance. Our foundation model dramatically improves the scalability and performance of a variety of downstream tasks.

over

90%

accuracy on key histopathology benchmarks*

*Dippel et al, 2024

up to

10%

increase in balanced accuracy of downstream tasks*

*based on internal analyses

up to

90%

less training data / annotations needed to train new models*

*based on internal analyses

What is a foundation model?

Trained on very large data sets, foundation models serve as a starting point for developing high performing machine learning models in a quick and cost-effective way.

Foundation models are able to use self-supervised learning and transfer leaning to apply information it has learnt about one situation to another, allowing it to be adaptable for any situation.

How was our foundation model developed?

Our foundation model for histopathology was developed using a similar approach as ChatGPT, starting with data input and having the original input reconstructed by AI to help the AI learn to understand images and their context.

Throughout the process, the data and outputs were verified with pathologists to ensure our model maintained a high level of accuracy.

How does our foundation model impact downstream tasks?

In internal analyses, our foundation model:

  • Reduces the amount of training data/annotations needed to optimize model performance by up to 90%

  • Increased the balanced accuracy of cell classification tasks by an average of ~10% across cell types 

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