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

Authors: Främling, K., Knapic̆, S. and Malhi, A.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 12688 LNAI

Pages: 55-62

eISSN: 1611-3349

ISBN: 9783030820169

ISSN: 0302-9743

DOI: 10.1007/978-3-030-82017-6_4

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.

http://eprints.bournemouth.ac.uk/36357/

Source: Scopus

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

Authors: Framling, K., Knapic, S. and Malhi, A.

Journal: EXPLAINABLE AND TRANSPARENT AI AND MULTI-AGENT SYSTEMS, EXTRAAMAS 2021

Volume: 12688

Pages: 55-62

eISSN: 1611-3349

ISBN: 978-3-030-82016-9

ISSN: 0302-9743

DOI: 10.1007/978-3-030-82017-6_4

http://eprints.bournemouth.ac.uk/36357/

Source: Web of Science (Lite)

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

Authors: Främling, K., Knapic̆, S. and Malhi, A.

Conference: EXTRAAMAS 2021: Third International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems

Pages: 55-62

ISBN: 9783030820169

ISSN: 0302-9743

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.

http://eprints.bournemouth.ac.uk/36357/

Source: BURO EPrints