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X-WR-CALDESC:Events for BioMedIA
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DTSTART:20160101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20170111T120000
DTEND;TZID=UTC:20170111T130000
DTSTAMP:20260418T145724
CREATED:20170104T151023Z
LAST-MODIFIED:20170105T114230Z
UID:3124-1484136000-1484139600@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Olaf Ronneberger (Google DeepMind / Albert-Ludwigs-Universität Freiburg)
DESCRIPTION:U-net: Convolutional Networks for Biomedical Image Segmentation \nAbstract: In this talk I will present our u-net for biomedical image segmentation. The architecture consists of an analysis path and a synthesis path with additional shortcut-connections. The network is trained end-to-end from scratch with a very low number of annotated images per application. The surprisingly simple strategy to train a network with such a low number of samples is a data augmentation with elastic deformations. Furthermore the u-net can segment arbitrarily large images with a seamless tiling strategy. For 3D images we have developed a training strategy to learn a full 3D segmentation from a few annotated slices per volume. This can be used in a semi-automated setup to create a dense segmentation from the sparse annotations on the same volume\, or in a fully-automated setup\, where sparse annotations speed up the training data generation significantly. \nReferences:\nO. Ronneberger\, P. Fischer\, and T. Brox: U-Net: Convolutional Networks for Biomedical Image Segmentation.\nMICCAI 2015\, available at http://arxiv.org/abs/1505.04597\nÖ. Çiçek\, A. Abdulkadir\, S. S. Lienkamp\, T. Brox\, O. Ronneberger: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. MICCAI 2016\, available at http://arxiv.org/abs/1606.06650
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-olaf-ronneberger-google-deepmind-albert-ludwigs-universitat-freiburg/
CATEGORIES:Talks & Seminars
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