Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:PACS-RIS Services.png|200px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
A [[picture archiving and communication system]] (PACS) is a digital imaging system composed of a set of components that allow for the digital acquisition, archiving, communication, retrieval, processing, distribution, and display of medical images. The PACS may consist of only a few components or be sufficiently complex to handle a hospital or healthcare enterprise environment. Regardless, it must be durable enough for daily use in a clinical environment, integrate to and from several [[Imaging informatics#Diagnostic imaging modalities|medical imaging modalities]], and have sufficient workstations for technicians utilizing those modalities to perform their work inside and outside the radiology department.[1] PACS benefit healthcare providers by digitally managing medical images, eliminating the need to manually file, retrieve, or transport film jackets. This often saves processing time in both the diagnostics and reporting related to the imagery, especially when integrated with speech recognition technology. ('''[[Picture archiving and communication system|Full article...]]''')<br />
'''"[[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]]"'''
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''Recently featured'': [[Cytopathology]], [[Clinical pathology]], [[Anatomical pathology]]
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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