Aktuelles

Poster Präsentation

Deep Generative Models 2019 (by MEC-Lab@GRIS)

Zeit: 16.07.2019, 10:00-11:00 Uhr
Ort: Raum 073 im Fraunhofer IGD, Fraunhoferstrasse 5, S3|05
Referent: Studierende der LV Deep Generative Models
Themenbeispiele:
1. Food Interpolator - fluid transition between pizza and burger
2. Generating Instagram images from hashtags
3. Interpolate over the space from the street view house numbers (SVHN) dataset

Gastvortrag

Zeit: 05.08.2019, 10:30-11:30 Uhr
Ort: Raum S101|A2, Universitätszentrum, karo 5, Karolinenplatz 5
Referent: Dr. Ilkay Oksuz
 King's College London Biomedical Engineering Department
Titel: "Automatic Quality Assessment of Cardiac MRI using Deep Learning Techniques"
Abstract: Cardiovascular disease (CAD) is the major cause of mortality in the world. Recently, Cardiovascular Magnetic Resonance (CMR) techniques have gained ground in diagnosis of cardiovascular disease and good quality of such MR images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. In this talk, recent work on detection of wrong cardiac planning and cardiac motion artefacts using deep learning techniques will be described. The details of deep learning architectures and machine learning methodologies will be given with a certain focus on synthetic k-space corruption and curriculum learning techniques. In the last part of the talk, the mechanisms to correct image artefacts will be discussed alongside with their influence on achieving high segmentation accuracy.
Bio: Dr. Ilkay Oksuz  is currently a Research Associate in King's College London Biomedical Engineering Department. His current research interests are in medical image segmentation, medical image registration and machine learning, with a focus on the automated analysis and quality control of cardiac MR. He studied for a PhD at the IMT Institute for Advanced Studies Lucca on Computer, Decision, and Systems Science under the supervision of Prof Sotirios Tsaftaris. His PhD thesis focused on joint registration and segmentation of the myocardium region in MR sequences. He joined the Diagnostic Radiology Group at Yale University in 2015 for 10 months as a Postgraduate Fellow, where he worked under the mentorship of Prof Xenios Papademetris. He also worked at the University of Edinburgh Institute for Digital Communications department for six months in 2017.
Referenzen:
https://link.springer.com/chapter/10.1007/978-3-030-00129-2_3
https://link.springer.com/chapter/10.1007/978-3-030-00928-1_29
https://www.sciencedirect.com/science/article/pii/S1361841518306765
https://ieeexplore.ieee.org/abstract/document/8363616
https://openreview.net/forum?id=BkgjbQ30yN

Workshop

GRIS organisiert den Workshop “Visual Analytics in Healthcare” (VAHC)
Datum: October 20th, 2019
Ort: IEEE VIS, Vancouver, Canada
Paper submission: https://new.precisionconference.com/submissions
Paper deadline: 17.Juni 2019
URL: https://www.visualanalyticshealthcare.org/
Dr. Jürgen Bernard ist einer der Organisatoren des VAHC-Workshops "Visual Analytics in Medicine 2019" am IEEE VIS, Vancouver, Kanada.
Visualisierungsexperten haben die Möglichkeit, Experten aus verschiedenen Bereichen der Medizin zu treffen. Die Teilnehmer dieses Workshops diskutieren gemeinsam innovative visuell-interaktive Lösungen für die Analyse medizinischer und patientenbezogener Daten. Expertengruppen diskutieren die Bereiche und Anwendungen von Ärzten und tauschen sich über Best-Practice-Ansätze aus. In diesem Jahr wird eine Zunahme von Lösungen erwartet, die explizit auf maschinelles Lernen und künstliche Intelligenztechniken zurückgreifen.
Teilnehmer an den Workshops können zwischen einem Full-Paper, einem Poster und einem Demo-Track wählen. Das Submission-System ist online!

 

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