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:Fig2 Heinen BMCBioinfo2020 21.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies|HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies]]"'''
'''"[[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]]"'''


Well-characterized biological specimens (biospecimens) of high quality have great potential for the acceleration of and quality improvement in [[Translational research|translational biomedical research]]. To improve accessibility of local [[Sample (material)|specimen]] collections, efforts have been made to create central repositories ([[biobank]]s) and catalogues. Available technical solutions for creating professional local specimen catalogues and connecting them to central systems are cost intensive and/or technically complex to implement. Therefore, the HIV-focused Thematic Translational Unit (TTU) of the German Center for Infection Research (DZIF) developed a [[laboratory information management system]] (LIMS) called HIV Engaged Research Technology (HEnRY) for implementation into the HIV Translational Platform (TP-HIV) at the DZIF and other research networks. ('''[[Journal:HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies|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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