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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Johnson JofCannRes23 5.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:Potency and safety analysis of hemp-derived delta-9 products: The hemp vs. cannabis demarcation problem|Potency and safety analysis of hemp-derived delta-9 products: The hemp vs. cannabis demarcation problem]]"'''
'''"[[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]]"'''


[[Hemp]]-derived [[Tetrahydrocannabinol|delta-9-tetrahydrocannabinol]] (Δ<sup>9</sup>-THC) products are freely available for sale across much of the USA, but the federal legislation allowing their sale places only minimal requirements on companies. Products must contain no more than 0.3% Δ<sup>9</sup>-THC by dry weight, but no limit is placed on overall dosage, and there is no requirement that products derived from hemp-based Δ<sup>9</sup>-THC be tested. However, some states—such as Colorado—specifically prohibit products created by “chemically modifying” a natural hemp component. Fifty-three hemp-derived Δ<sup>9</sup>-THC products were ordered and submitted to InfiniteCAL [[laboratory]] for analysis ... ('''[[Journal:Potency and safety analysis of hemp-derived delta-9 products: The hemp vs. cannabis demarcation problem|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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