Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/23618
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nicolaou, Andria | - |
dc.contributor.author | Loizou, Christos P. | - |
dc.contributor.author | Pantzaris, Marios C. | - |
dc.contributor.author | Kakas, Antonis | - |
dc.contributor.author | Pattichis, Constantinos S. | - |
dc.date.accessioned | 2021-11-12T06:29:22Z | - |
dc.date.available | 2021-11-12T06:29:22Z | - |
dc.date.issued | 2021-09 | - |
dc.identifier.citation | 19th International Conference on Computer Analysis of Images and Patterns, 2021, 28-30 September, Virtual Event | en_US |
dc.identifier.isbn | 978-3-030-89128-2 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/23618 | - |
dc.description.abstract | Various artificial intelligence (AI) algorithms have been proposed in the literature, that are used as medical assistants in clinical diagnostic tasks. Explainability methods are lighting the black-box nature of these algorithms. The objective of this study was the extraction of rules for the assessment of brain magnetic resonance imaging (MRI) lesions in Multiple Sclerosis (MS) subjects based on texture features. Rule extraction of lesion features was used to explain and provide information on the disease diagnosis and progression. A dataset of 38 subjects diagnosed with a clinically isolated syndrome (CIS) of MS and MRI detectable brain lesions were scanned twice with an interval of 6–12 months. MS lesions were manually segmented by an experienced neurologist. Features were extracted from the segmented MS lesions and were correlated with the expanded disability status scale (EDSS) ten years after the initial diagnosis in order to quantify future disability progression. The subjects were separated into two different groups, G1: EDSS ≤ 3.5 and G2: EDSS > 3.5. Classification models were implemented on the KNIME analytics platform using decision trees (DT), to estimate the models with high accuracy and extract the best rules. The results of this study show the effectiveness of rule extraction as it can differentiate MS subjects with benign course of the disease (G1: EDSS ≤ 3.5) and subjects with advanced accumulating disability (G2: EDSS > 3.5) using texture features. Further work is currently in progress to incorporate argumentation modeling to enable rule combination as well as better explainability. The proposed methodology should also be evaluated on more subjects. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.rights | © Springer | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Multiple Sclerosis | en_US |
dc.subject | Brain MRI | en_US |
dc.subject | Lesions | en_US |
dc.subject | Texture features | en_US |
dc.subject | Classification | en_US |
dc.subject | Rule extraction | en_US |
dc.subject | Explainability | en_US |
dc.title | Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings | en_US |
dc.type | Conference Papers | en_US |
dc.collaboration | University of Cyprus | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | Cyprus Institute of Neurology and Genetics | en_US |
dc.subject.category | Medical Engineering | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.relation.conference | International Conference on Computer Analysis of Images and Patterns | en_US |
dc.identifier.doi | 10.1007/978-3-030-89128-2_27 | en_US |
cut.common.academicyear | 2021-2022 | en_US |
dc.identifier.spage | 277 | en_US |
dc.identifier.epage | 286 | 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 | 0000-0003-1247-8573 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
CORE Recommender
SCOPUSTM
Citations
5
checked on Nov 6, 2023
Page view(s)
332
Last Week
2
2
Last month
31
31
checked on Mar 14, 2025
Google ScholarTM
Check
Altmetric
This item is licensed under a Creative Commons License