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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Zheng Electronics23 12-8.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:Registered data-centered lab management system based on data ownership security architecture|Registered data-centered lab management system based on data ownership security architecture]]"'''
'''"[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]"'''


University and college [[Laboratory|laboratories]] are important places to train professional and technical personnel. Various regulatory departments in colleges and universities still rely on traditional laboratory management in [[research]] projects, which are prone to problems such as untimely [[information]] and data transmission. The present study aimed to propose a new method to solve the problem of data islands, explicit ownership, conditional sharing, [[Information security|data security]], and efficiency during laboratory [[Information management|data management]]. Hence, this study aimed to develop a data-centered lab management system that enhances the security of laboratory data management and allows the data owners of the labs to control [[data sharing]] with other users ... ('''[[Journal:Registered data-centered lab management system based on data ownership security architecture|Full article...]]''')<br />
Full [[laboratory automation]] is revolutionizing work habits in an increasing number of clinical [[microbiology]] facilities worldwide, generating huge streams of [[Imaging|digital images]] for interpretation. Contextually, [[deep learning]] (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic [[Bacteria|bacterial]] [[Cell culture|culture]] plates, including presumptive [[pathogen]] identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... ('''[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Full article...]]''')<br />
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Latest revision as of 15:02, 3 June 2024

Fig1 Signoroni NatComm23 14.png

"Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology"

Full laboratory automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... (Full article...)
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