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

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMalhi, Avleenen_US
dc.contributor.authorKampik, Timotheusen_US
dc.contributor.authorPannu, Husanbir Singhen_US
dc.contributor.authorMadhikermi, Maniken_US
dc.contributor.authorFrämling, Karyen_US
dc.contributor.departmentFrämling Kary groupen_US
dc.contributor.departmentThapar Institute of Engineering and Technologyen_US
dc.contributor.departmentUmeå Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.date.accessioned2020-02-03T09:03:39Z
dc.date.available2020-02-03T09:03:39Z
dc.date.issued2019-12en_US
dc.description.abstractThis 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.en
dc.description.versionPeer revieweden
dc.format.extent7
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMalhi , A , Kampik , T , Pannu , H S , Madhikermi , M & Främling , K 2019 , Explaining Machine Learning-based Classifications of in-vivo Gastral Images . in 2019 Digital Image Computing : Techniques and Applications (DICTA) . , 8945986 , IEEE , International Conference on Digital Image Computing: Techniques and Applications , Perth , Australia , 02/12/2019 . https://doi.org/10.1109/DICTA47822.2019.8945986en
dc.identifier.doi10.1109/DICTA47822.2019.8945986en_US
dc.identifier.isbn978-1-7281-3857-2
dc.identifier.isbn978-1-7281-3856-5
dc.identifier.otherPURE UUID: ef63e754-34c6-4f1e-9632-14410ac38b97en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/ef63e754-34c6-4f1e-9632-14410ac38b97en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/40465094/SCI_Malhi_Explaining_Machine_.08945986.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/42968
dc.identifier.urnURN:NBN:fi:aalto-202002032048
dc.language.isoenen
dc.relation.ispartofInternational Conference on Digital Image Computing: Techniques and Applicationsen
dc.relation.ispartofseries2019 Digital Image Computing: Techniques and Applications (DICTA)en
dc.rightsopenAccessen
dc.titleExplaining Machine Learning-based Classifications of in-vivo Gastral Imagesen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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