Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey
This survey provides a systematic overview for researchers and practitioners in fields like medicine and industry, but it is incremental as it compiles existing work without introducing new methods.
This paper presents the first comprehensive survey on explainable AI (XAI) in semantic image segmentation, addressing the gap in attention compared to classification-based techniques, and it analyzes literature across applications, domains, and evaluation metrics while proposing a taxonomy and discussing future challenges.
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.