TY - GEN
T1 - Explainable Artificial Intelligence for Cytological Image Analysis
AU - Röhrl, Stefan
AU - Maier, Hendrik
AU - Lengl, Manuel
AU - Klenk, Christian
AU - Heim, Dominik
AU - Knopp, Martin
AU - Schumann, Simon
AU - Hayden, Oliver
AU - Diepold, Klaus
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Blood Cell Analysis
KW - Quantitative Phase Imaging
KW - XAI
UR - http://www.scopus.com/inward/record.url?scp=85164013412&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34344-5_10
DO - 10.1007/978-3-031-34344-5_10
M3 - Conference contribution
AN - SCOPUS:85164013412
SN - 9783031343438
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 75
EP - 85
BT - Artificial Intelligence in Medicine - 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Proceedings
A2 - Juarez, Jose M.
A2 - Marcos, Mar
A2 - Stiglic, Gregor
A2 - Tucker, Allan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Artificial Intelligence in Medicine, AIME 2023
Y2 - 12 June 2023 through 15 June 2023
ER -