Cookie Settings

We use cookies to optimize our website. These include cookies that are necessary for the operation of the site, as well as those that are only used for anonymous statistic. You can decide for yourself which categories you want to allow. Further information can be found in our data privacy protection .


These cookies are necessary to run the core functionalities of this website and cannot be disabled.

Name Webedition CMS
Purpose This cookie is required by the CMS (Content Management System) Webedition for the system to function correctly. Typically, this cookie is deleted when the browser is closed.
Name econda
Purpose Session cookie emos_jcsid for the web analysis software econda. This runs in the “anonymized measurement” mode. There is no personal reference. As soon as the user leaves the site, tracking is ended and all data in the browser are automatically deleted.

These cookies help us understand how visitors interact with our website by collecting and analyzing information anonymously. Depending on the tool, one or more cookies are set by the provider.

Name econda
Purpose Statistics
External media

Content from external media platforms is blocked by default. If cookies from external media are accepted, access to this content no longer requires manual consent.

Name YouTube
Purpose Show YouTube content
Name Twitter
Purpose activate Twitter Feeds



2021-2023 – funded by the Ministry for Social Affairs, Health and Integration of Baden-Württemberg


The ultimate goal of the AI translation initiative (KTI) project is to create the base for a successful introduction of diagnostic AI based image analysis algorithms into the daily clinical practice by improving their explainability (XAI) as well as generalisation.

The current focus of the project is on pathological diagnostics.


Initially, the XAI methods are evaluated from a technical point of view to determine whether they accurately and reproducibly reflect and elucidate functioning and decision-making of the diagnostic algorithms. The primary aspects to be analyzed and compared are correctness and robustness. Potential users will be interviewed to evaluate the influence of the explainability techniques on acceptance of AI-based assistance systems. Additionally, the direct usefulness of such systems regarding diagnostic precision will be determined.


For the clinical application of KI based assistance systems, one question is of particular importance: In which cases can the practitioner trust the AI-based classification and in which cases can he or she not? For this particular decision, information on the confidence level of the system’s decision might be rather important for the user of a KI based assistance system and might help to estimate the limitations of the system. If the confidence value for a certain image is low, then the training of the model was not sufficient for a well-informed prediction. This could be due to the particular training method that was used and/or to the image being an outlier that is not sufficiently represented in the training data.


Another central requirement for a successful introduction of KI based diagnostic assistance systems into clinical practice is a reliable applicability also an unknown, ‘foreign’ data. Especially in the KI based analysis of histological tissue sections, even small differences in the staining protocol between labs represent a huge challenge. In order to increase the generalization ability of algorithms, different methods (such as normalization and augmentation techniques as well as few shot learning approaches) are tested.

nach oben
powered by webEdition CMS