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Causal Inference in Radiomics: Framework, Mechanisms, and Algorithms
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최윤성 | - |
dc.date.accessioned | 2022-12-22T02:22:51Z | - |
dc.date.available | 2022-12-22T02:22:51Z | - |
dc.date.issued | 2022-06 | - |
dc.identifier.issn | 1662-4548 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/191553 | - |
dc.description.abstract | The widespread use of machine learning algorithms in radiomics has led to a proliferation of flexible prognostic models for clinical outcomes. However, a limitation of these techniques is their black-box nature, which prevents the ability for increased mechanistic phenomenological understanding. In this article, we develop an inferential framework for estimating causal effects with radiomics data. A new challenge is that the exposure of interest is latent so that new estimation procedures are needed. We leverage a multivariate version of partial least squares for causal effect estimation. The methodology is illustrated with applications to two radiomics datasets, one in osteosarcoma and one in glioblastoma. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Frontiers Research Foundation | - |
dc.relation.isPartOf | FRONTIERS IN NEUROSCIENCE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Causal Inference in Radiomics: Framework, Mechanisms, and Algorithms | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Debashis Ghosh | - |
dc.contributor.googleauthor | Emily Mastej | - |
dc.contributor.googleauthor | Rajan Jain | - |
dc.contributor.googleauthor | Yoon Seong Choi | - |
dc.identifier.doi | 10.3389/fnins.2022.884708 | - |
dc.contributor.localId | A04137 | - |
dc.relation.journalcode | J02867 | - |
dc.identifier.eissn | 1662-453X | - |
dc.identifier.pmid | 35812228 | - |
dc.subject.keyword | latent causal effect | - |
dc.subject.keyword | link-free inference | - |
dc.subject.keyword | medical imaging | - |
dc.subject.keyword | personalized medicine | - |
dc.subject.keyword | sufficient dimension reduction | - |
dc.contributor.alternativeName | Choi, Yoon Seong | - |
dc.contributor.affiliatedAuthor | 최윤성 | - |
dc.citation.volume | 16 | - |
dc.citation.startPage | 884708 | - |
dc.identifier.bibliographicCitation | FRONTIERS IN NEUROSCIENCE, Vol.16 : 884708, 2022-06 | - |
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