EXplainable Artificial Intelligence (XAI) for MRI brain tumor diagnosis: A survey

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 ch...

Full description

Saved in:
Bibliographic Details
Main Authors: Charaabi, Hana, Mzoughi, Hiba, Hamdi, Ridha, Njah, Mohamed
Format: Conference Proceeding
Language:English
Subjects:
brain tumor diagnosis
eXplainable Artificial Intelligence
intrinsic XAI
Medical Imaging Data
MRI
post-hoc XAI
XAI
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2642-3596
DOI:10.1109/CW58918.2023.00033