Explaining Machine Learning-based Classifications of in-vivo Gastral Images

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

Researchers

Research units

  • Thapar Institute of Engineering and Technology - TIET
  • Umeå University

Abstract

This paper proposes an explainable machine learning tool that can potentially be used for decision support in medical image analysis scenarios. For a decision-support system it is important to be able to reverse-engineer the impact of features on the final decision outcome. In the medical domain, such functionality is typically required to allow applying machine learning to clinical decision making. In this paper, we present initial experiments that have been performed on in-vivo gastral images obtained from capsule endoscopy. Quantitative analysis has been performed to evaluate the utility of the proposed method. Convolutional neural networks have been used for training the validating of the image data set to provide the bleeding classifications. The visual explanations have been provided in the images to help health professionals trust the black box predictions. While the paper focuses on the in-vivo gastral image use case, most findings are generalizable.

Details

Original languageEnglish
Title of host publication2019 Digital Image Computing
Subtitle of host publicationTechniques and Applications (DICTA)
Publication statusPublished - Dec 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Digital Image Computing: Techniques and Applications - Hyatt Regency Perth, Perth, Australia
Duration: 2 Dec 20194 Dec 2019

Conference

ConferenceInternational Conference on Digital Image Computing: Techniques and Applications
Abbreviated titleDICTA
CountryAustralia
CityPerth
Period02/12/201904/12/2019

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