Abstract
Enormous potential of artificial intelligence (AI) exists in numerous products and services, especially in healthcare and medical technology. Explainability is a central prerequisite for certification procedures around the world and the fulfilment of transparency obligations. Explainability tools increase the comprehensibility of object recognition in images using Convolutional Neural Networks, but lack precision.
This paper adapts FastCAM for the domain of detection of medical instruments in endoscopy images. The results show that the Domain Adapted (DA)-FastCAM provides better results for the focus of the model than standard FastCAM weights.
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Acknowledgment
The authors would like to acknowledge the financial support from the German Federal Ministry of Research and Education (Bundesministerium für Bildung und Forschung) under grant CoHMed/PersonaMed A for this research. Thanks to the DigNest project the results will be disseminated at seminars and workshops.
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Stodt, J., Reich, C., Clarke, N. (2022). Explainable AI with Domain Adapted FastCAM for Endoscopy Images. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_6
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DOI: https://doi.org/10.1007/978-3-031-08757-8_6
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