Cephalometric landmark detection via global and local encoders and patch-wise attentions
DC Field | Value | Language |
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dc.contributor.author | Lee, Minkyung | - |
dc.contributor.author | Chung, Minyoung | - |
dc.contributor.author | Shin, Yeong-Gil | - |
dc.date.accessioned | 2022-03-11T07:40:20Z | - |
dc.date.available | 2022-03-11T07:40:20Z | - |
dc.date.created | 2022-03-11 | - |
dc.date.issued | 2022-01-22 | - |
dc.identifier.issn | 0925-2312 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/42044 | - |
dc.description.abstract | Cephalometric landmark detection performs an important role in a diagnostic measurements for orthodontic treatment plans. As manual depiction of landmarks is a time-consuming and tedious task, the development of an automated detection algorithm for daily clinics is in high demand. In this study, we propose a single-passing convolutional neural network that performs an accurate landmark detection in a hierarchical fashion. The proposed network first extracts global contexts by regressing initial positions of all the landmarks. Subsequently, local features are extracted from landmark-centered patches, which are obtained through global regression. The encoded global and local features are concatenated and weighed through a novel patch-wise attention module to weigh the relative importance. The experimental results demonstrate that our proposed local patch-wise attention mechanism performs a significant role in accurate detection. The proposed method outperformed other state-of-the-art methods by improving the successful detection rate by approximately 1 ti 2%. The proposed method suggests that a structured attention, which is developed in a patch-wise fashion, significantly enhances the local feature encoders to further improve the final accuracy. (c) 2021 Elsevier B.V. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.relation.isPartOf | NEUROCOMPUTING | - |
dc.title | Cephalometric landmark detection via global and local encoders and patch-wise attentions | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.neucom.2021.11.003 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | NEUROCOMPUTING, v.470, pp.182 - 189 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000722305600016 | - |
dc.identifier.scopusid | 2-s2.0-85119251334 | - |
dc.citation.endPage | 189 | - |
dc.citation.startPage | 182 | - |
dc.citation.title | NEUROCOMPUTING | - |
dc.citation.volume | 470 | - |
dc.contributor.affiliatedAuthor | Chung, Minyoung | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Auxiliary decoder networks | - |
dc.subject.keywordAuthor | Cephalometric landmark detection | - |
dc.subject.keywordAuthor | Direct point regression | - |
dc.subject.keywordAuthor | Explicit global and local feature fusion | - |
dc.subject.keywordAuthor | Patch-wise attention | - |
dc.subject.keywordPlus | X-RAY IMAGES | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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