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<title><string language="fre"><![CDATA[Dr Natalie Reznikov - Application of deep learning for segmentation of 3D images in biomineralization]]></string></title>
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<string language="fre"><![CDATA[Modern 3D imaging methods in biomineralization –
such as X-ray tomography and dual-beam electron tomography – produce datasets
that are rich in fine detail and enormous in size, often containing inevitable
artifacts.  Rendering segmentations of
such datasets is a daunting task.  The
recent introduction of artificial neural network-based deep learning into bioimaging
has made 3D segmentation reliable, accurate and fast.  A highlight of convolutional neural networks
(CNNs) is that artificial "neurons" are interlinked hierarchically, similarly
to how feature-forming patterns of an image are related.  Accordingly, when a raw image is presented to
a deep net, the neurons of different layers perceive the patterns of different
complexity.  Upper-level neurons detect small
patterns within their local context, and the local context itself forms patterns
for deeper neuronal layers, and within a larger context, and so on.  Thus, identification of features based on overt
(e.g. contrast, gradient) and covert patterns
(e.g. level of noise, wavelet
frequency) becomes not only accurate, but also generalizable.  Once image patterns can be accurately enough
identified as being features of interest – and thus the CNN is “trained” – such
patterns can be segmented automatically on any similar image.  In machine learning, as in biological
learning, the accuracy of pattern detection and classification improves with
experience.  Once trained, a CNN can be
treated like an image filter – easy to preview, fast to apply, simple to share,
and handy to reuse.  In this
presentation, I will explain the essence of deep learning and CNN operation for
non-computer scientists, and will illustrate this with examples of “difficult”
3D images (a chick embryo inside a fertilized egg, and coral).]]></string></description>
<keyword><string language="fre"><![CDATA[3d modeling]]></string></keyword>
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<date><dateTime>2021-03-23</dateTime></date>
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<string language="fre"><![CDATA[Droits réservés à l'éditeur et aux auteurs. 
@ LE STUDIUM 2021]]></string>
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<description>
<string language="fre"><![CDATA[Innate immunity in a biomineralized context: trade-offs or synergies?]]></string>
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