ciu.image: An R Package for Explaining Image Classification with Contextual Importance and Utility

Kary Främling*, Samanta Knapic̆, Avleen Malhi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

Many techniques have been proposed in recent years that attempt to explain results of image classifiers, notably for the case when the classifier is a deep neural network. This paper presents an implementation of the Contextual Importance and Utility method for explaining image classifications. It is an R package that can be used with the most usual image classification models. The paper shows results for typical benchmark images, as well as for a medical data set of gastro-enterological images. For comparison, results produced by the LIME method are included. Results show that CIU produces similar or better results than LIME with significantly shorter calculation times. However, the main purpose of this paper is to bring the existence of this package to general knowledge and use, rather than comparing with other explanation methods.

Original languageEnglish
Title of host publicationExplainable and Transparent AI and Multi-Agent Systems - 3rd International Workshop, EXTRAAMAS 2021, Revised Selected Papers
EditorsDavide Calvaresi, Amro Najjar, Michael Winikoff, Kary Främling
Pages55-62
Number of pages8
DOIs
Publication statusPublished - 2021
MoE publication typeA4 Article in a conference publication
EventInternational Workshop on Explainable, Transparent AI and Multi-Agent Systems - Virtual, Online
Duration: 3 May 20217 May 2021
Conference number: 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume12688 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

WorkshopInternational Workshop on Explainable, Transparent AI and Multi-Agent Systems
Abbreviated titleEXTRAAMAS
CityVirtual, Online
Period03/05/202107/05/2021

Keywords

  • Contextual importance and utility
  • Deep neural network
  • Explainable artificial intelligence
  • Image classification

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