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AI in Digital Pathology – Robustness and Explainability

xAI – Explainable AI in Digital Pathology

© BMBF

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

Solution:

  • 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)

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Literature:
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, https://www.trillium.de/zeitschriften/trillium-diagnostik/ausgaben-2019/td-heft-22019/pathologie/einsatz-von-kuenstlicher-intelligenz-in-der-digitalen-pathologie.html

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

Database

  • In development

Solution:

  • 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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Engineering. Imaging. Sensors. Analysis.

Current and finished Products and Projects

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