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2022 | OriginalPaper | Buchkapitel

CAIPI in Practice: Towards Explainable Interactive Medical Image Classification

verfasst von : Emanuel Slany, Yannik Ott, Stephan Scheele, Jan Paulus, Ute Schmid

Erschienen in: Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops

Verlag: Springer International Publishing

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Abstract

Would you trust physicians if they cannot explain their decisions to you? Medical diagnostics using machine learning gained enormously in importance within the last decade. However, without further enhancements many state-of-the-art machine learning methods are not suitable for medical application. The most important reasons are insufficient data set quality and the black-box behavior of machine learning algorithms such as Deep Learning models. Consequently, end-users cannot correct the model’s decisions and the corresponding explanations. The latter is crucial for the trustworthiness of machine learning in the medical domain. The research field explainable interactive machine learning searches for methods that address both shortcomings. This paper extends the explainable and interactive CAIPI algorithm and provides an interface to simplify human-in-the-loop approaches for image classification. The interface enables the end-user (1) to investigate and (2) to correct the model’s prediction and explanation, and (3) to influence the data set quality. After CAIPI optimization with only a single counterexample per iteration, the model achieves an accuracy of \(97.48\%\) on the Medical MNIST and \(95.02\%\) on the Fashion MNIST. This accuracy is approximately equal to state-of-the-art Deep Learning optimization procedures. Besides, CAIPI reduces the labeling effort by approximately \(80\%\).

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Metadaten
Titel
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification
verfasst von
Emanuel Slany
Yannik Ott
Stephan Scheele
Jan Paulus
Ute Schmid
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-031-08341-9_31