UH Biocomputation Group - Image analysishttp://biocomputation.herts.ac.uk/2020-11-09T10:28:15+00:00Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging2020-11-09T10:28:15+00:002020-11-09T10:28:15+00:00Minghua Zhengtag:biocomputation.herts.ac.uk,2020-11-09:/2020/11/09/bacterial-colony-counting-with-convolutional-neural-networks-in-digital-microbiology-imaging.html<p class="first last">Minghua Zheng's Journal Club session where he will talk about the paper "Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging".</p>
<p>This week on Journal Club session Minghua Zheng will talk about the paper "Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging".</p>
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<p>Counting bacterial colonies on microbiological culture plates is a
time-consuming, error-prone, nevertheless essential quantitative task in Clinica
Microbiology Laboratories. With this work we explore the possibility to find
effective solutions to the above issue by designing and testing two different
machine learning approaches. The first one is based on the extraction of a
complete set of handcrafted morphometric and radiometric features used within a
Support Vector Machines solution. The second one is based on the design and
configuration of a Convolutional Neural Networks deep learning architecture. To
validate, in a real and challenging clinical scenario, the proposed bacterial
load estimation techniques, we built and publicly released a fully labeled large
and representative database of both single and aggregated bacterial colonies
extracted from routine clinical laboratory culture plates. Dataset enhancement
approaches have also been experimentally tested for performance optimization.
The adopted deep learning approach outperformed the handcrafted feature based
one, and also a conventional reference technique, by a large margin, becoming a
preferable solution for the addressed Digital Microbiology Imaging quantification
task, especially in the emerging context of Full Laboratory Automation systems.</p>
<p>Papers:</p>
<ul class="simple">
<li>Alessandro Ferrari, Stefano Lombardia, Alberto Signoroni. (2017). <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0031320316301650">"Bacterial colony counting with Convolutional Neural Networks inDigital Microbiology Imaging"</a> , Pattern Recognition, Volume 61, January 2017, Pages 629-640</li>
</ul>
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<p><strong>Date:</strong> 13/11/2020 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: online</p>