BioEdge: Accelerating Object Detection in Bioimages with Edge-Based Distributed Inferenceopen access
- Authors
- Ahn, Hyunho; Lee, Munkyu; Seong, Sihoon; Lee, Minhyeok; Na, Gap-Joo; Chun, In-Geol; Kim, Youngpil; Hong, Cheol-Ho
- Issue Date
- Nov-2023
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Keywords
- bioimage analysis; distributed inference; edge computing; object detection
- Citation
- Electronics (Switzerland), v.12, no.21
- Journal Title
- Electronics (Switzerland)
- Volume
- 12
- Number
- 21
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/68835
- DOI
- 10.3390/electronics12214544
- ISSN
- 2079-9292
2079-9292
- 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.
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