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X-WR-CALNAME:BioMedIA
X-ORIGINAL-URL:https://biomedia.doc.ic.ac.uk
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:20260414T172812
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170222T120000
DTEND;TZID=UTC:20170222T130000
DTSTAMP:20260414T172812
CREATED:20170212T110255Z
LAST-MODIFIED:20170212T110310Z
UID:3170-1487764800-1487768400@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Matthan Caan (Academic Medical Centre & Spinoza Centre for Neuroimaging\, Amsterdam)
DESCRIPTION:Quantifying MRI: from reconstruction to application in neuroimaging \nAbstract: To understand the origin and development of neurological disorders\, Magnetic Resonance Imaging (MRI) has proven to provide valuable quantitative measures. In this overview presentation\, I will touch upon several topics. Volumetric measures obtained via segmentation find their application in prenatal famine exposure\, HIV and ischemic stroke. We developed a scattering transform that has no learnable parameters for computing convolutional neural networks in small patient studies. Diffusion MRI is a sensitive method for detecting microstructural changes. We assessed the reproducibility of different models in a multi-site context using a complex diffusion phantom. T1\, T2* and Quantitative Susceptibility Mapping (QSM) allow for mapping myelin and iron content at high resolution at 7T. We present a single MRI sequence for obtaining these measures in a time efficient manner. Further acceleration may be achieved by compressed sensing and potentially deep learning.
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-matthan-caan/
CATEGORIES:Talks & Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170327T120000
DTEND;TZID=UTC:20170327T130000
DTSTAMP:20260414T172812
CREATED:20170324T121949Z
LAST-MODIFIED:20170324T121949Z
UID:3364-1490616000-1490619600@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Raphael Sznitman (University of Bern\, Switzerland)
DESCRIPTION:Finding the needle in the haystack: detection\, tracking and registration in biomedical imaging\n  \nAbstract\n\n\n\nFrom centimeter-sized observations visible in endoscopy to nanometer large intra-cellular structures discernible with Electron Microscopes\, searching and locating objects of interest in images is a central problem in medical image computing. If anything\, the need for efficient object detection techniques has never been higher due to the advent of cheaper and ever more sophisticated imaging devices\, able of amassing unprecedented quantities of data. And while established search paradigms are showing their limits\, faster methods capable of dealing with larger quantities of data are now indispensable. \n\n\n\nIn this talk\, I will present a computational framework for object detection with efficiency at its core. This framework\, a Bayesian formulation of the traditional “twenty questions” game\, considers a sequential strategy for evaluating different parts of the image data in order to locate the object effectively. In this context\, we will see how dynamic programming and information theory can be used to characterize a provably-optimal search strategy that is both simple to compute and greedy in nature. Using these results\, I will then show how this framework can be used to solve traditional object detection and tracking problems\, as well as non-rigid registration of multimodal multi-scale image data\, allowing for more accurate solutions and large problems to be tackled. \n\n\n\nShort Bio:\nRaphael Sznitman received his B.Sc. in cognitive science from the University of British Columbia (Canada) in 2007. Following this\, he studied Computer Science at Johns Hopkins University where he received his M.Sc and PhD in 2011. From 2011 to 2014\, he was a postdoctoral fellow at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. Now an Assistant Professor at the ARTORG Center for Biomedical Engineering Research of the University of Bern (Switzerland)\, his research interests lie in the fields of computer vision and machine learning with applications to biomedical imaging\, surgery and histology.
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-raphael-sznitman-university-of-bern-switzerland/
LOCATION:Huxley 341\, Room 341\, Huxley Building\, Department of Computing\, Imperial College London\, 180 Queen's Gate\, London\, London\, SW7 2AZ\, United Kingdom
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170328T113000
DTEND;TZID=UTC:20170328T123000
DTSTAMP:20260414T172812
CREATED:20170324T121905Z
LAST-MODIFIED:20170324T121905Z
UID:3375-1490700600-1490704200@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Wenzhe Shi (Twitter)
DESCRIPTION:Neural network’s for super resolution\n  \nAbstract\nThe most important parts for using neural network’s for super resolution are data\, network architecture and the objective functions. In this talk we will discuss our innovation in network architecture to make our algorithms running real time on mobile devices as well as how we achieved a new level of perceived qualities for super resolved images by redefining the object functions. Finally we will close the talk by showing what’s the best practice when extending image super resolution techniques to videos.
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-wenzhe-shi-twitter/
LOCATION:Huxley 218
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170829T120000
DTEND;TZID=UTC:20170829T130000
DTSTAMP:20260414T172812
CREATED:20170819T063022Z
LAST-MODIFIED:20170828T124722Z
UID:3545-1504008000-1504011600@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Loic Le Folgoc (Microsoft Research)
DESCRIPTION:Title: Learning structure in complex data: machine learning for medical image segmentation\, registration and shape analysis
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-loic-le-folgoc-microsoft-research/
CATEGORIES:Talks & Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170928T110000
DTEND;TZID=UTC:20170928T120000
DTSTAMP:20260414T172812
CREATED:20170926T082146Z
LAST-MODIFIED:20170926T082211Z
UID:3571-1506596400-1506600000@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Polina Golland (MIT CSAIL)
DESCRIPTION:Title: MRI of placenta: from pixels to function
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-polina-golland-mit-csail/
CATEGORIES:Talks & Seminars
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