Abstract
Deep Neural Networks (DNNs) have shown tremendous success in numerous AI-related fields. However, despite DNNs exhibiting remarkable performance, they still can make mistakes. Therefore, estimation and quantification of uncertainty have become an essential parameter in Deep Learning practical applications, especially in medical imaging. Measuring uncertainty can help with better decision making, early diagnosis, and a variety of tasks. In this paper, we explore uncertainty quantification (UQ) approaches and propose an uncertainty quantification (UQ) system for general medical imaging classification tasks. For its practical use, we adapt our UQ system for the problem of medical pre-screening, where patients are referred to a medical specialist if the DNN model classification or diagnosis is too uncertain. In experiments, we apply the UQ system to two medical imaging databases, a SARS-CoV2 CT dataset and the BreaKHis dataset. We show how to capture the most uncertain samples and predict the most uncertain category. For the application of medical pre-screening, we demonstrate that we can obtain more accurate results than initial modeling results by removing a percentage of the most uncertain input data.
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Yang, S., Fevens, T. (2021). Uncertainty Quantification and Estimation in Medical Image Classification. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_54
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