Abstract
The digitization of pathology opens up a wide field of applications that can be supported by AI-based analysis like the detection of tumors or a quantitative assessment of tissue composition. This contribution demonstrates possible ways on how to approach challenges in digital pathology like the robustness against data heterogeneity or the detection of out-of-distribution data. Moreover, the principle of prototypical few-shot models is explained, which can be adapted to new tasks with only a few labeled examples without any retraining of the underlying model parameters. In this chapter we show the suitability of a prototypical few-shot classification model for tumor detection in two different organs and a prototypical few-shot segmentation model for tumor composition analysis. Finally, a workflow for the creation of a dedicated AI model by only providing a few annotations within the MIKAIA® software of Fraunhofer IIS is presented
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Benz, M., Kuritcyn, P., Kletzander, R., Bruns, V. (2024). Robust and Adaptive AI for Digital Pathology. In: Mutschler, C., Münzenmayer, C., Uhlmann, N., Martin, A. (eds) Unlocking Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-031-64832-8_12
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DOI: https://doi.org/10.1007/978-3-031-64832-8_12
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Publisher Name: Springer, Cham
Print ISBN: 978-3-031-64831-1
Online ISBN: 978-3-031-64832-8
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