AI in Digital Pathology – Robustness and Explainability

xAI – Explainable AI in Digital Pathology


Goal: turn AI algorithms used in Digital Pathology from a black box into a glass box

Partners: University of Bamberg (Cognitive Systems, Prof. Dr. Ute Schmid), Fraunhofer HHI

Current Status:

  • Research and evaluation of technologies to make AI algorithms more explainable


  • Display results from neural networks in an easily comprehensible and transparent manner
  • Take into account uncertainties from the annotated training data as well from the model itself

Funded by Federal Ministry of Education and Research (BMBF)
Transparent Medical Expert Companion (TraMeExCo)


Schmid, Wittenberg (2019) TraMeExCo - Medical Expert Companion (Vortrag). All-Hands-Meeting (5. Juni 2019, Dortmund), Förderschwerpunkt Maschinelles Lernen des BMBF. -- [Slides], [Poster]
Rieger, Finzel, Seuß, Wittenberg, Schmid (2019) Make Pain Estimation Transparent: A Roadmap to Fuse Bayesian Deep Learning and Inductive Logic Programming (Poster). 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC, 23.-27. Juli 2019, Berlin)

Bruns, Volker, Geppert, Carol (2019), Einsatz von Künstlicher Intelligenz in der digitalen Pathologie - Auf dem Sprung in die Routine?, Trillium Diagnostik 2/2019,

Poster presentation at Global Engage blog: Tumor budding in brightfield immunostained colon sections


Development of Robust AI systems for Digital Pathology

Goal: design AI algorithms that are robust to real-world heterogeneity (different scanners, staining variances, sub-optimal slide quality)

Current Status:

  • Developed algorithms for simulating staining variances
  • Creating multi-scanner database of H&E stained colon section
  • Developing Prototypical Networks (PN) for Few-Shot Learning: this will enable the development of new classifiers using only little data and few annotations


  • In development


  • Represent real-world heterogeneity in training database or normalize data to match training data characteristics

Research Project: ADA Center – Applications Data Analytics
Subproject: Explainable AI in Life Science and Automotive

More Information

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Medical Image Analysis – Publications

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