Explainable Artificial Intelligence for Cytological Image Analysis

Stefan Röhrl, Hendrik Maier, Manuel Lengl, Christian Klenk, Dominik Heim, Martin Knopp, Simon Schumann, Oliver Hayden, Klaus Diepold

Abstract: Emerging new technologies are entering the medical market. Among them, the use of Machine Learning (ML) is becoming more common. This work explores the associated Explainable Artificial Intelligence (XAI) approaches, which should help to provide insight into the often opaque methods and thus gain trust of users and patients as well as facilitate interdisciplinary work. Using the differentiation of white blood cells with the aid of a high throughput quantitative phase microscope as an example, we developed a web-based XAI dashboard to assess the effect of different XAI methods on the perception and the judgment of our users. Therefore, we conducted a study with two user groups of data scientists and biomedical researchers and evaluated their interaction with our XAI modules, with respect to the aspects of behavioral understanding of the algorithm, its ability to detect biases and its trustworthiness. The results of the user tests show considerable improvement achieved through the XAI dashboard on the measured set of aspects. A deep dive analysis aggregated on the different user groups compares the five implemented modules. Furthermore, the results reveal that using a combination of modules achieves higher appreciation than the individual modules. Finally, one observes a user’s tendency of overestimating the trustworthiness of the algorithm compared to their perceived abilities to understand the behavior of the algorithm and to detect biases.