Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty.

ABSTRACT: Complex deep learning models have shown their impressive power in analyzing high-dimensional medical image data. To increase the trust of applying deep learning models in medical field, it is essential to understand why a particular prediction was reached. Data feature importance estimation is an important approach to understand both the model and the underlying properties of data. Shapley value explanation (SHAP) is a technique to fairly evaluate input feature importance of a given model. However, the existing SHAP-based explanation works have limitations such as 1) computational complexity, which hinders their applications on high-dimensional medical image data; 2) being sensitive to noise, which can lead to serious errors. Therefore, we propose an uncertainty estimation method for the f

SUBMITTER: Li X 

PROVIDER: S-EPMC8299327 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature