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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig11 Davies PLOSCompBio20 16-11.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:Using interactive digital notebooks for bioscience and informatics education|Using interactive digital notebooks for bioscience and informatics education]]"'''
'''"[[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]]"'''


Interactive digital notebooks provide an opportunity for researchers and educators to carry out data analysis and report results in a single digital format. Further to just being digital, the format allows for rich content to be created in order to interact with the code and data contained in such a notebook to form an educational narrative. This primer introduces some of the fundamental aspects involved in using [[Jupyter Notebook]] in an educational setting for teaching in the [[bioinformatics]] and [[health informatics]] disciplines. We also provide two case studies that detail 1. how we used Jupyter Notebooks to teach non-coders programming skills on a blended master’s degree module for a health informatics program, and 2. a fully online distance learning unit on programming for a postgraduate certificate (PG Cert) in clinical bioinformatics, with a more technical audience. ('''[[Journal:Using interactive digital notebooks for bioscience and informatics education|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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