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Robust multimodal fusion network using adversarial learning for brain tumor grading

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dc.contributor.authorSeung-wan Jeong-
dc.contributor.authorCho, Hwan-ho-
dc.contributor.authorLee, Seunghak-
dc.contributor.authorHyunjin Park-
dc.date.accessioned2023-01-26T02:35:25Z-
dc.date.available2023-01-26T02:35:25Z-
dc.date.created2022-10-29-
dc.date.issued2022-11-
dc.identifier.issn0169-2607-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/12615-
dc.description.abstract© 2022Background and Objective: Gliomas are graded using multimodal magnetic resonance imaging, which provides important information for treatment and prognosis. When modalities are missing, the grading is degraded. We propose a robust brain tumor grading model that can handle missing modalities. Methods: Our method was developed and tested on Brain Tumor Segmentation Challenge 2017 dataset (n = 285) via nested five-fold cross-validation. Our method adopts adversarial learning to generate the features of missing modalities relative to the features obtained from a full set of modalities in the latent space. An attention-based fusion block across modalities fuses the features of each available modality into a shared representation. Our method's results are compared to those of two other models where 15 missing-modality scenarios are explicitly considered and a joint training approach with random dropouts is used. Results: Our method outperforms the two competing methods in classifying high-grade gliomas (HGGs) and low-grade gliomas (LGGs), achieving an area under the curve of 87.76% on average for all missing-modality scenarios. The activation maps derived with our method confirm that it focuses on the enhancing portion of the tumor in HGGs and on the edema and non-enhancing portions of the tumor in LGGs, which is consistent with prior expertise. An ablation study shows the added benefits of a fusion block and adversarial learning for handling missing modalities. Conclusion: Our method shows robust grading of gliomas in all cases of missing modalities. Our proposed network might have positive implications in glioma care by learning features robust to missing modalities.-
dc.language영어-
dc.publisherElsevier Ireland Ltd-
dc.titleRobust multimodal fusion network using adversarial learning for brain tumor grading-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000872389100006-
dc.identifier.scopusid2-s2.0-85139369640-
dc.identifier.rimsid79013-
dc.contributor.affiliatedAuthorSeung-wan Jeong-
dc.contributor.affiliatedAuthorHyunjin Park-
dc.identifier.doi10.1016/j.cmpb.2022.107165-
dc.identifier.bibliographicCitationComputer Methods and Programs in Biomedicine, v.226-
dc.relation.isPartOfComputer Methods and Programs in Biomedicine-
dc.citation.titleComputer Methods and Programs in Biomedicine-
dc.citation.volume226-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMedical Informatics-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.relation.journalWebOfScienceCategoryEngineering, Biomedical-
dc.relation.journalWebOfScienceCategoryMedical Informatics-
dc.subject.keywordAuthorAdversarial learning-
dc.subject.keywordAuthorFusion block-
dc.subject.keywordAuthorGlioma grading-
dc.subject.keywordAuthorMissing modalities-
dc.subject.keywordAuthorRobust classification-
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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