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VERSION:2.0
PRODID:-//Danish BioImaging Network - ECPv4.9.1.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.danishbioimaging.dk
X-WR-CALDESC:Events for Danish BioImaging Network
BEGIN:VEVENT
DTSTART;VALUE=DATE:20200120
DTEND;VALUE=DATE:20200125
DTSTAMP:20260716T101719
CREATED:20190805T170133Z
LAST-MODIFIED:20190805T170133Z
UID:3397-1579478400-1579910399@www.danishbioimaging.dk
SUMMARY:Deep Learning for Image Analysis
DESCRIPTION:Course Overview\nThis is a blended learning course on Deep Learning for Image Analysis\, consisting of 3 online sessions with associated hands-on exercises and a week-long onsite session at EMBL Heidelberg. \nAudience\nThis course is aimed at both core facility staff and research scientists. \nPrerequisites for this workshop are programming skills in Python and ideally Tensorflow\, Keras or Pytorch as well as basic knowledge of machine learning theory. \nParticipants should provide an outline of one image analysis task they would like to work on during the on-site part of the course. Neural networks have been successfully applied to various medical and biological imaging modalities including PALM/STORM\, light sheet fluorescence microscopy\, high-throughput microscopy\, electron microscopy\, X-ray tomography. However\, they require observation-outcome-pairs for training. Ideally\, you will be provide annotated images for network training during the course. \nLearning Outcomes\nAfter this course you should be able to: \n\nUnderstand the fundamentals of machine learning methods suitable for image analysis\nAdvise users/colleagues in strategies to obtain ground truth\nTrain and use a CNN for a bioimage analysis task studied in the course\nPerform simple quality control on the results\n\n
URL:https://www.danishbioimaging.dk/event/deep-learning-for-image-analysis/
LOCATION:EMBL Heidelberg\, Germany
CATEGORIES:Courses
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