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PRODID:-//Danish BioImaging Network - ECPv4.9.1.1//NONSGML v1.0//EN
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X-ORIGINAL-URL:https://www.danishbioimaging.dk
X-WR-CALDESC:Events for Danish BioImaging Network
BEGIN:VEVENT
DTSTART;VALUE=DATE:20181029
DTEND;VALUE=DATE:20181101
DTSTAMP:20260717T142329
CREATED:20180516T084216Z
LAST-MODIFIED:20180517T091104Z
UID:2272-1540771200-1541030399@www.danishbioimaging.dk
SUMMARY:Machine Learning for Image Analysis
DESCRIPTION:\n\n\n\n\n\n\n\n\n\nDate: Monday 29 – Wednesday 31 October 2018 \n\n\nVenue: EMBL – EMBL- Heidelberg\, Meyerhofstraße\, 69117\,  Heidelberg\, Germany \n\n\nApplication opens: Monday May 07 2018 \n\n\nApplication deadline: Friday June 15 2018 \n\n\nParticipation: Open application with selection \n\n\nContact: Charlotte Pearton \n\n\nRegistration fee: £0 \n\n\nDates additional information: Up-front online sessions: 2nd\, 9th and 16th October 2018 (12:00-14:00 CEST) On-site workshop: 29-31st October 2018 Follow-up online sessions (optional): 9th and 16th November 2018 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCourse Overview\n\nThis is a blended learning course on Machine Learning for Image Analysis\, consisting of three online sessions with associated hands-on exercises prior to the workshop\, a three day face-to-face workshop at EMBL Heidelberg and two optional online sessions with associated hands-on exercises after the workshop. The course is jointly organised by CORBEL\, EMBL\, German BioImaging and NEUBIAS (COST Action CA15124).\nThe course will be a great mix of intensive learning\, extensive hands-on and community networking in the field of Machine Learning for Image Analysis. \n\nUp-front online sessions: Participants will review the fundamentals of machine learning in three up-front webinars complemented by online tutorials. The webinars will take place on 2nd\, 9th and 16th October 2018\, 12:00 – 14:00 but a recorded alternative can be provided.\nOn-site workshop: Next\, they will apply their knowledge at an on-site workshop (EMBL Heidelberg\, October 29-31)\, in small interactive groups (the workshop has 20 available seats and ~8 trainer/lecturer)\, to both reference datasets and their own data. Topics to be practiced in these groups include: 2D and 3D segmentation with convolutional neural networks\, Content aware image restoration\, simulation of ground truth data\, labeling strategies and transfer learning. Participants will be asked to pick one group for the practical sessions.\nFollow-up online sessions (optional): After the on-site workshop\, two optional advanced training webinar\, complemented by a online tutorials\, will be given on 9th and 16th November 2018. These will focus simulation of data\, transfer learning and boosting.\n\nPractical Sessions (participants will be asked to choose one during registration):\nDetection and Segmentation in microscopic images (Thorsten Falk): \n\nTopics: Segmentation with convolutional neural networks\, Pattern in bio-medical image data\nTypical data: Results of multi-labeling high content screens\nTools: Python\, Keras\, Tensorflow\n\nSegmentation in 3D microscopy image stacks (Anna Kreshuk & Constantin Pape): \n\nTopics: 3D segmentation (with convolutional neural networks)\, 2D\, 2.5D and 3D networks and their combinations\, Pre- and post-processing tricks\, Combination of deep and “shallow” learning\nTypical data: 3D datasets recorded by light or electron microscopy\nTools: Python\, Keras\, PyTorch\n\nTransfer learning and how to use synthetic data for supervised deep learning (David Rousseau & Pejman Rasti): \n\nTopic: Basics of transfer learning\nTypical data: PALM/STORM\, 3D cells in spheroid imaged in light sheet fluorescence microscopy and 3D plant roots images in absorption X-ray tomography\nTools: Python\, Keras\, Tensorflow\n\nContent-aware image restoration (Martin Weigert): \n\nTopics: Content aware image restoration\, Simulation of images for light microscopy\nTypical data: Pairs of light microscopic images imaged in ideal and suboptimal conditions\nTools: Python\, Keras\, Tensorflow\n\n\n\n\nAudience\n\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 that holds potential to be ideally solved with machine learning. 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 provide annotated images. \n\n\n\n\n\n\n\n\n\n\n\n\n\nLearning outcomes\n\nAfter this course you should be able to: \n\nExplain the fundamentals of machine learning methods suitable for image analysis\nConsult users/colleagues in strategies to obtain ground truth\nGive advice in training and using a neural-network\nPerform simple quality control on the results of one selected ML approach\n\n\n\n\n\n\n\n\n
URL:https://www.danishbioimaging.dk/event/machine-learning-for-image-analysis/
LOCATION:EMBL Heidelberg\, EMBL HEIDELBERG Meyerhofstraße 1 69117 Heidelberg\, Germany Tel: +49 6221 387-0 Fax: +49 6221 387-8306
CATEGORIES:Courses
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