Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey

TL;DR

This survey reviews explainable AI techniques in semantic image segmentation across various fields, highlighting current methods, challenges, and future directions for dense prediction interpretability.

Contribution

It provides the first comprehensive taxonomy and analysis of XAI methods specifically tailored for semantic image segmentation tasks.

Findings

Categorized segmentation XAI techniques by application and domain

Analyzed evaluation metrics and datasets used in the field

Identified challenges and proposed future research directions

Abstract

Artificial Intelligence (XAI) has found numerous applications in computer vision. While image classification-based explainability techniques have garnered significant attention, their counterparts in semantic segmentation have been relatively neglected. Given the prevalent use of image segmentation, ranging from medical to industrial deployments, these techniques warrant a systematic look. In this paper, we present the first comprehensive survey on XAI in semantic image segmentation. This work focuses on techniques that were either specifically introduced for dense prediction tasks or were extended for them by modifying existing methods in classification. We analyze and categorize the literature based on application categories and domains, as well as the evaluation metrics and datasets used. We also propose a taxonomy for interpretable semantic segmentation, and discuss potential challenges and future research directions.