BioEdge: Accelerating Object Detection in Bioimages with Edge-Based Distributed Inference
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Hyunho | - |
dc.contributor.author | Lee, Munkyu | - |
dc.contributor.author | Seong, Sihoon | - |
dc.contributor.author | Lee, Minhyeok | - |
dc.contributor.author | Na, Gap-Joo | - |
dc.contributor.author | Chun, In-Geol | - |
dc.contributor.author | Kim, Youngpil | - |
dc.contributor.author | Hong, Cheol-Ho | - |
dc.date.accessioned | 2023-12-05T11:41:12Z | - |
dc.date.available | 2023-12-05T11:41:12Z | - |
dc.date.issued | 2023-11 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/68835 | - |
dc.description.abstract | Convolutional neural networks (CNNs) have enabled effective object detection tasks in bioimages. Unfortunately, implementing such an object detection model can be computationally intensive, especially on resource-limited hardware in a laboratory or hospital setting. This study aims to develop a framework called BioEdge that can accelerate object detection using Scaled-YOLOv4 and YOLOv7 by leveraging edge computing for bioimage analysis. BioEdge employs a distributed inference technique with Scaled-YOLOv4 and YOLOv7 to harness the computational resources of both a local computer and an edge server, enabling rapid detection of COVID-19 abnormalities in chest radiographs. By implementing distributed inference techniques, BioEdge addresses privacy concerns that can arise when transmitting biomedical data to an edge server. Additionally, it incorporates a computationally lightweight autoencoder at the split point to reduce data transmission overhead. For evaluation, this study utilizes the COVID-19 dataset provided by the Society for Imaging Informatics in Medicine (SIIM). BioEdge is shown to improve the inference latency of Scaled-YOLOv4 and YOLOv7 by up to 6.28 times with negligible accuracy loss compared to local computer execution in our evaluation setting. © 2023 by the authors. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | - |
dc.title | BioEdge: Accelerating Object Detection in Bioimages with Edge-Based Distributed Inference | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/electronics12214544 | - |
dc.identifier.bibliographicCitation | Electronics (Switzerland), v.12, no.21 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 001099433300001 | - |
dc.identifier.scopusid | 2-s2.0-85176147051 | - |
dc.citation.number | 21 | - |
dc.citation.title | Electronics (Switzerland) | - |
dc.citation.volume | 12 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | bioimage analysis | - |
dc.subject.keywordAuthor | distributed inference | - |
dc.subject.keywordAuthor | edge computing | - |
dc.subject.keywordAuthor | object detection | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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