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Towards Trust of Explainable AI in Thyroid Nodule Diagnosis

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Artificial Intelligence for Personalized Medicine (W3PHAI 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1106))

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Abstract

The ability to explain the prediction of deep learning models to end-users is an important feature to leverage the power of artificial intelligence (AI) for the medical decision-making process, which is usually considered non-transparent and challenging to comprehend. In this paper, we apply state-of-the-art eXplainable artificial intelligence (XAI) methods to explain the prediction of the black-box AI models in the thyroid nodule diagnosis application. We propose new statistic-based XAI methods, namely Kernel Density Estimation and Density map, to explain the case of no nodule detected. XAI methods’ performances are considered under a qualitative and quantitative comparison as feedback to improve the data quality and the model performance. Finally, we survey to assess doctors’ and patients’ trust in XAI explanations of the model’s decisions on thyroid nodule images.

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Notes

  1. 1.

    https://github.com/hungntt/xai_thyroid.

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Author information

Authors and Affiliations

  1. Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

    Truong Thanh Hung Nguyen

  2. Quy Nhon AI, FPT Software, Quy Nhon, Vietnam

    Truong Thanh Hung Nguyen, Van Binh Truong, Vo Thanh Khang Nguyen, Quoc Hung Cao & Quoc Khanh Nguyen

Authors
  1. Truong Thanh Hung Nguyen
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  2. Van Binh Truong
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  3. Vo Thanh Khang Nguyen
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  4. Quoc Hung Cao
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  5. Quoc Khanh Nguyen
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Corresponding author

Correspondence to Truong Thanh Hung Nguyen .

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Editors and Affiliations

  1. Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center–Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Memphis, TN, USA

    Arash Shaban-Nejad

  2. School of Nursing, University of Minnesota, Minneapolis, MN, USA

    Martin Michalowski

  3. Altos Labs - Bay Area Institute of Science, Redwood City, CA, USA

    Simone Bianco

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Nguyen, T.T.H., Truong, V.B., Nguyen, V.T.K., Cao, Q.H., Nguyen, Q.K. (2023). Towards Trust of Explainable AI in Thyroid Nodule Diagnosis. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) Artificial Intelligence for Personalized Medicine. W3PHAI 2023. Studies in Computational Intelligence, vol 1106. Springer, Cham. https://doi.org/10.1007/978-3-031-36938-4_2

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