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SUMMARY:[ON-SITE] Vision Models Use Cases with AI
DTSTART:20261013T070000Z
DTEND:20261013T100000Z
DTSTAMP:20261004T025600Z
UID:indico-event-410@events.it4i.cz
CONTACT:training@it4i.cz
DESCRIPTION:\nAnnotation\nThe goal of this course is to teach participants
  how to prepare their own training data primarily for image segmentation\,
  possibly also for image classification\, and to show them creating of an 
 own model of a neural network that specifically addresses their own image 
 data and task. Throughout the course\, basic terms and theory will be expl
 ained and\, above all\, shared practical experience will be provided throu
 gh short\, light-weight lectures.\nAll work will rely solely on SW tools w
 ith open licenses that do not exclude commercial use. The principal tool w
 ill be Fiji\, a graphical desktop application (an ordinary program with a 
 window\, operated with a mouse) for image processing. This is a well-known
  program that is also continuously developed towards remote and large data
 \, and that can be found on essentially every biologist's desktop in scien
 tific and academic settings. Within Fiji\, we will use the Labkit plugin\,
  which allows images to be annotated and segmented\, either manually by dr
 awing with the mouse or semi-automatically\, AI-assisted. Labkit offers on
 ly neural networks\, such as Cellpose or Segment-Anything-Model\, that wor
 k in isolation (no user data leaves a computer)\, and are relatively undem
 anding computationally. Finally\, we will demonstrate a simple Python modu
 le for training new models and show how to bring such a model back into L
 abkit\, as well as how to use it directly\, whether locally or remotely.\n
 Benefits for the attendees\nAttendees will learn how to:\n\nPrepare and an
 notate their own image datasets for segmentation and classification.\nUse 
 Fiji and Labkit for manual and AI-assisted image segmentation.\nTrain a ne
 ural network tailored to their own images and research tasks.\nDeploy trai
 ned models in Labkit or use them independently\, both locally and remotely
 .\nBuild privacy-conscious workflows using open-source tools suitable for 
 commercial use.\n\nThe course combines essential theory with hands-on guid
 ance and practical experience.\nLevel\nIntermediate\nLanguage\nEnglish\nPr
 erequisites\nThe course assumes participants primarily from biology\, medi
 cine\, and related fields.\nTechnical requirements\n\nData to work on will
  be freely available\, though participants are welcome to bring their own 
 data.\nParticipants shall bring their own laptops. Power plugs will be ava
 ilable\, operating systems Windows\, Linux\, and Macs are all supported\, 
 5 GB of disk space shall be available for the course. \nThe laptops need 
 to be set up prior to the course according to the provided video instructi
 ons. \nBasic computer literacy is sufficient for creating annotations. \
 n\nTutor\nDr. Vladimír Ulman is a computer scientist specializing in biom
 edical image processing and analysis\, with a focus on large-scale bioimag
 e data. He earned his PhD in computer science in 2011. Currently\, he hold
 s appointments at the IT4Innovations National Supercomputing Center (VSB 
 – Technical University of Ostrava) and at CEITEC\, Masaryk University\, 
 in Brno. He develops open-source tools mainly within the Fiji/ImageJ ecosy
 stem and gives hands-on training in bioimage analysis\, including Fiji/Ima
 geJ\, Napari\, and AI tools for image processing whenever opportunities ar
 ise.\n \n \n\n \nLUMI AI Factory is funded jointly by the EuroHPC Joint
  Undertaking\, through the European Union's Connecting Europe Facility and
  the Horizon 2020 research and innovation programme\, as well as Finland\,
  the Czech Republic\, Poland\, Estonia\, Norway\, and Denmark.\nThis cours
 e was supported by the Ministry of Education\, Youth and Sports of the Cze
 ch Republic through the e-INFRA CZ (ID:90254).\n\nAll presentations and ed
 ucational materials of this course are provided under the Creative Commons
  Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. \n\nhtt
 ps://events.it4i.cz/event/410/
LOCATION:Training room 207\, IT4Innovations (ON-SITE)
URL:https://events.it4i.cz/event/410/
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