machine learning knowledge extraction digital pathology


Together with the Institute of Pathology (Prof. Kurt Zatloukal), based on the ICT-2011.9.5 – FET Flagship Initative Preparatory Action “IT Future of Medicine” and in a joint effort together with and the ADOPT project, we are working on theoretical, algorithmic, and experimental studies on machine learning and knowledge extraction from digital pathology.  The work of pathologists is interesting for several reasons: 1) Digital pathology is not just the transformation of the classical microscopic analysis of histological slides by pathologists to a digital visualization, it is an innovation that will dramatically change medical workflows in the coming years; 2) Much information is hidden in arbitrarily high dimensional spaces, not accessible to a human, consequently we need machine learning approach to generate a new kind of information, which is not yet available and not exploited in current diagnostics; 3) Pathologists are able to transfer previously learned knowledge quickly to new tasks. Insights into the latter supports machine learning research and may contribute to answer a grand question: How can machine learning algorithms perform a task by exploiting knowledge, extracted during solving previous tasks? Contributions to solve this problem would have major impact to Artificial Intelligence generally, and Machine Learning specifically. This implies to develop software which can learn from experience – similarly as we humans do. However, what is mandatory is to make the decisions explainable and interpretable.

  • Technical Area

    Deep Learning, interactive Machine Learning, geometrical approaches

  • Application Area

    Digital Pathology

  • Recent Publications

    [1]    Holzinger, A., Malle, B., Kieseberg, P., Roth, P. M., Müller, H., Reihs, R. & Zatloukal, K. 2017. Machine Learning and Knowledge Extraction in Digital Pathology needs an integrative approach. Springer Lecture Notes in Artificial Intelligence Volume LNAI 10344. Cham: Springer International, pp. 13-50.