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

Source: [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](<https://www.sota2.com/research/paper/68fa73181e209131cea5a7f5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:42:27.853Z

Authors: Rokas Gipi\\v{s}kis, Chun-Wei Tsai, Olga Kurasova

Published: 2024-05-02T18:00:25.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2405.01636>)

[PDF](<https://arxiv.org/pdf/2405.01636.pdf>)