Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/30043
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Prentzas, Nicoletta | - |
dc.contributor.author | Pitsiali, Marios | - |
dc.contributor.author | Kyriacou, Efthyvoulos C. | - |
dc.contributor.author | Nicolaides, Andrew N. | - |
dc.contributor.author | Kakas, Antonis | - |
dc.contributor.author | Pattichis, Constantinos S. | - |
dc.date.accessioned | 2023-08-03T06:04:50Z | - |
dc.date.available | 2023-08-03T06:04:50Z | - |
dc.date.issued | 2021-10-25 | - |
dc.identifier.citation | 21st IEEE International Conference on BioInformatics and BioEngineering, BIBE 2021, Kragujevac, 25 - 27 October 2021 | en_US |
dc.identifier.isbn | 9781665442619 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/30043 | - |
dc.description.abstract | The current adoption of Medical Artificial Intelligence (AI) solutions in clinical practice suggest that despite its undeniable potential AI is not achieving this potential. A major barrier to its adoption is the lack of transparency and interpretability, and the inability of the system to explain its results. Explainable AI (XAI) is an emerging field in AI that aims to address these barriers, with the development of new or modified algorithms to enable transparency, provide explanations in a way that humans can understand and foster trust. Numerous XAI techniques have been proposed in the literature, commonly classified as model-agnostic or model-specific. In this study, we examine the application of four model-agnostic XAI techniques (LIME, SHAP, ANCHORS, inTrees) to an XGBoost classifier trained on real-life medical data for the prediction of high-risk asymptomatic carotid plaques based on ultrasound image analysis. We present and compare local explanations for selected observations in the test set. We also present global explanations generated from these techniques that explain the behavior of the entire model. Additionally, we assess the quality of the explanations, using suggested properties in the literature. Finally, we discuss the results of this comparative study and suggest directions for future work. | en_US |
dc.language.iso | en | en_US |
dc.rights | © IEEE | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | ANCHORs | en_US |
dc.subject | explainable AI | en_US |
dc.subject | inTrees | en_US |
dc.subject | LIME | en_US |
dc.subject | model-agnostic explain ability | en_US |
dc.subject | SHAP | en_US |
dc.title | Model Agnostic Explainability Techniques in Ultrasound Image Analysis | en_US |
dc.type | Conference Papers | en_US |
dc.collaboration | University of Cyprus | en_US |
dc.collaboration | Frederick University | en_US |
dc.collaboration | Vascular Screening and Diagnostic Centre | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.relation.conference | BIBE 2021 - 21st IEEE International Conference on BioInformatics and BioEngineering | en_US |
dc.identifier.doi | 10.1109/BIBE52308.2021.9635199 | en_US |
dc.identifier.scopus | 2-s2.0-85123720821 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85123720821 | - |
cut.common.academicyear | 2021-2022 | en_US |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.cerifentitytype | Publications | - |
item.openairetype | conferenceObject | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-4589-519X | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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