EXplainable Artificial Intelligence (XAI) for MRI brain tumor diagnosis: A survey
An overview on the application of XAI in Deep Learning-based Magnetic Resonance Imaging (MRI) image analysis for Brain Tumor (BT) diagnosis is presented and three groups of post-hoc XAI methods improved the confidence on the DL based brain tumor diagnosis.
Abstract
The results of the Deep Learning (DL) are indisputable in different fields and in particular that of the medical diagnosis. The black box nature of this tool has left the doctors very cautious with regard to its estimates. The eXplainable Artificial Intelligence (XAI) recently seemed to lift this challenge by providing explanations to the DL estimates. Several works are published in the literature offering explanatory methods. We are interested in this survey to present an overview on the application of XAI in Deep Learning-based Magnetic Resonance Imaging (MRI) image analysis for Brain Tumor (BT) diagnosis. In this survey, we divide these XAI methods into four groups, the group of the intrinsic methods and three groups of post-hoc methods which are the activation based, the gradientr based and the perturbation based XAI methods. These XAI tools improved the confidence on the DL based brain tumor diagnosis.