ArXiv Preprint
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.
Truong Thanh Hung Nguyen, Van Binh Truong, Vo Thanh Khang Nguyen, Quoc Hung Cao, Quoc Khanh Nguyen
2023-03-08
