Uncertainty Quantification in Skin Cancer Classification using Three-Way Decision-based Bayesian Deep Learning

Abstract Accurate automated medical image recognition, including classification and segmentation, is one of the most challenging tasks in medical image analysis. Recently, deep learning methods have achieved remarkable success in medical image classification and segmentation, clearly becoming the state-of-the-art methods. However, most of these methods are unable to provide uncertainty quantification (UQ) for their output, often being overconfident, which can lead to disastrous consequences. Bayesian Deep Learning (BDL) methods can be used to quantify uncertainty of traditional deep learning methods, and thus address this issue. We apply three uncertainty quantification methods to deal with uncertainty during skin cancer image classification. They are as follows: Monte Carlo (MC) dropout, Ensemble MC (EMC) dropout and Deep Ensemble (DE). To further resolve the remaining uncertainty after applying the MC, EMC and DE methods, we describe a novel hybrid dynamic BDL model, taking into account uncertainty, based on the Three-way Decision (TWD) theory. The proposed dynamic model enables us to use various UQ methods and different deep neural networks in distinct phases. So, the elements of each phase can be adjusted according to the dataset under consideration. In this study, two UQ methods are applied in two phases to analyze two well-known skin datasets, thus preventing one from making overconfident decisions when it comes to diagnosing the disease. The accuracy and the F1-score of the final solution are, respectively, 88.95% and 89.00% for the first considered dataset, and 90.96% and 91.00% for the second considered dataset. Our results suggest that the proposed model has the potential to be used effectively at different stages of medical image analysis.