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DTSTAMP:20260617T154613Z
UID:7f77598d-bf63-4e36-b06e-67fa33d9b605
DTSTART:20210908T140000Z
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DESCRIPTION:Educators:\nJanina Mothes\, Temesgen H. Dadi (CIBI)\n\nDate:\nS
 eptember 8th\, 2021\n\nLocation:\nGCB 2021 - Online\n\nContents:\nImage an
 alysis is one of the hallmarks of biomedical research due to its wide rang
 e of potential applications. This includes enhancing our understanding of 
 brain function by analyzing the connectivity of individual neuronal proces
 ses and synapses through serial transmission electron microscopy (EM). Mac
 hine learning approaches\, in particular convolutional neural networks\, a
 llow the automatic segmentation of neural structures in EM images\, an imp
 ortant step towards automating the extraction of neuronal connectivity.\nT
 he open source KNIME Analytics Platform offers an accessible tool based on
  the visual programming paradigm to analyse diverse kinds of data\, includ
 ing images. In addition\, one can choose from a wide array of data transfo
 rmations\, machine learning algorithms\, and visualizations and combine th
 ose in one reproducible workflow. KNIME Analytics Platform is freely avail
 able from ​https://www.knime.com/downloads​.\n\nIn this hands-on tutor
 ial\, participants will produce a workflow to create and train a specific 
 Convolutional Network (U-Net) for segmenting cell images. We will start by
  importing and cleaning up the input data (Transmission Electron Microscop
 y data). Afterwards\, with the help of the KNIME Tensorflow2 integration\,
  we will then train a U-Net model and use the trained network to predict t
 he segmentation of unseen data. In the last step\, we visualize our result
 s.\n\nLearning goals:\nParticipants will learn how to\n- Use the open sour
 ce KNIME Analytics Platform for importing\, blending and transforming data
 \n- Work with images in KNIME Analytics Platform\n- Train a U-Net model an
 d apply it to unseen data\n- Visualize the results\n\nPrerequisites:\nFor 
 a hands-on tutorial\, participants need to bring their own laptop. All the
  necessary software and data will be made available for download before th
 e tutorial day.\n\nStudents (grad/undergrad)\, researchers\, principal inv
 estigators with an interest in machine learning\, images\, data manipulati
 on are welcome to attend the tutorial. A little background on machine lear
 ning and imaging data is a plus. We will provide a short introduction to t
 he KNIME Analytics Platform\, cell segmentation\, and convolutional neural
  networks\, before starting the hands-on sessions.\n\nKeywords:\nComputati
 onal Workflow\, KNIME\, Image analysis\n\nTools:\nKNIME
SUMMARY:Cell segmentation using KNIME Analytics Platform and its Tensorflow
 2 Integration - GCB 2021
URL;VALUE=URI:https://www.denbi.de/training/1276-cell-segmentation-using-kn
 ime-analytics-platform-and-its-tensorflow2-integration
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