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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Oyashi GeospatialHlth2019 14-1.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:Japan Aerospace Exploration Agency’s public-health monitoring and analysis platform: A satellite-derived environmental information system supporting epidemiological study|Japan Aerospace Exploration Agency’s public-health monitoring and analysis platform: A satellite-derived environmental information system supporting epidemiological study]]"'''
'''"[[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]]"'''


Since the 1970s, Earth-observing satellites collect increasingly detailed [[Environmental monitoring|environmental information]] on land cover, meteorological conditions, environmental variables, and air pollutants. This [[information]] spans the entire globe, and its acquisition plays an important role in epidemiological analysis when ''in situ'' data are unavailable or spatially and/or temporally sparse. In this paper, we present the development of the Japan Aerospace Exploration Agency’s (JAXA) Public-health Monitoring and Analysis Platform, a user-friendly, web-based system providing environmental data on shortwave radiation, rainfall, soil moisture, the normalized difference vegetation index, aerosol optical thickness, land surface temperature and altitude. ('''[[Journal:Japan Aerospace Exploration Agency’s public-health monitoring and analysis platform: A satellite-derived environmental information system supporting epidemiological study|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...)
Recently featured: