Current Trends in Medical Imaging Acquisition and Communication

Stoian, Andrei; Ivan, R.; Stoian, I.; Marichescu, A.

doi:10.1109/aqtr.2008.4588890

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…1 FIGURE 1. General Federated Learning Architecture FL has unique use cases, with plenty of research relating to FL's applications, one example being in the healthcare sector [19], [20]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

et al. 2020

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This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.

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Section: Introductionmentioning

confidence: 99%

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

et al. 2020

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“…During the last few years the Picture Archiving and Communication System (PACS) that is the most popular for health system architecture has changed from radiographic film to digital imaging called a filmless system [1][2][3][4][5]. The PACS or filmless information system is an a combination of hardware and software, which is used to capture, store, distribute and display images with the Digital Imaging and Communications in Medicine (DICOM) standard.…”

Section: Introductionmentioning

confidence: 99%

Picture archiving and communication system (PACS) characteristic on wired-line and wireless network for traffic simulation

Chimmanee

Patpituck

2013

The International Conference on Information Networking 2013 (ICOIN)

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Recently, characteristic of HTTP, FTP, SMTP, VoIP and other Internet applications are widely studied. It is very useful for Traffic Engineering (TE) and Traffic Simulation. PACS (Picture Archiving and Communication System) has been becoming a popular healthcare application for accessing to medical images and patient's data diagnostics on a wired-line or/and wireless hospital network. However, a characteristic of PACS traffic on both wired-line and wireless network is rarely studied. Therefore, this paper studies and presents the characteristic of PACS over the wired-line and wireless network based on terms of the packet interval time, the packet size distribution (byte), as well as the effect of variable PACS flows (traffic load) on the ping packet loss, which are on the actual measurement. In addition, the packet interval time is represented in the form of PDF. The packet size distribution is represented in the form of histogram graph. It is very useful for Traffic Simulation.

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