Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges

TL;DR

This paper reviews the current state of explainable AI in biomedical image and signal processing, highlighting its potential to improve interpretability and clinical translation, along with existing challenges and solutions.

Contribution

It provides a comprehensive overview of XAI methods in biomedical data processing and discusses future directions and ongoing research efforts.

Findings

XAI enhances interpretability of biomedical models

Various XAI techniques are being developed for biomedical applications

Challenges remain in integrating XAI into clinical workflows

Abstract

Artificial intelligence has become pervasive across disciplines and fields, and biomedical image and signal processing is no exception. The growing and widespread interest on the topic has triggered a vast research activity that is reflected in an exponential research effort. Through study of massive and diverse biomedical data, machine and deep learning models have revolutionized various tasks such as modeling, segmentation, registration, classification and synthesis, outperforming traditional techniques. However, the difficulty in translating the results into biologically/clinically interpretable information is preventing their full exploitation in the field. Explainable AI (XAI) attempts to fill this translational gap by providing means to make the models interpretable and providing explanations. Different solutions have been proposed so far and are gaining increasing interest from the community. This paper aims at providing an overview on XAI in biomedical data processing and points to an upcoming Special Issue on Deep Learning in Biomedical Image and Signal Processing of the IEEE Signal Processing Magazine that is going to appear in March 2022.