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  4. Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. Proceedings
 
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2019
Conference Proceeding
Title

Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. Proceedings

Title Supplement
First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019
Abstract
On October 17, 2019, the 8th International Workshop on Clinical Image-based Procedures: From Planning to Intervention (CLIP 2019) was held in Shenzhen, China in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019). Following the tradition set in the last seven years, this year's edition of the workshop was an exciting forum for the discussion and dissemination of clinically tested, state-of-the-art methods for image-based planning, monitoring, and evaluation of medical procedures. Nowadays, it has become more and more important for many clinical applications to base decisions not only on image data alone, thus a focus of CLIP 2019 was the creation of holistic patient models. Here, image data such as radiologic images, microscopy images, and photographs are combined with non-image information such as 'omics' data (e.g. genomics, proteomics), lifestyle data, demographics, EEG, and others to build a more complete picture of the individual patient and to subsequently provide better diagnosis and therapies. CLIP 2019 provided a forum for work centered on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data. Submissions related to applications already in use and evaluated by clinical users were particularly encouraged. Furthermore, novel techniques and applications that are looking at combining image analysis with clinical data mining and analytics, user studies, and other heterogeneous data were a focus as well. In CLIP's 8th edition, world-class researchers and clinicians came together to present ways to strengthen links between computer scientists and engineers, and surgeons, interventional radiologists, and radiation oncologists.
Editor(s)
Greenspan, Hayit
Tel Aviv Univ., Israel
Tanno, Ryutaro
University College London, UK
Erdt, Marius  
Fraunhofer Singapore  
Arbel, Tal
McGill University, Montreal, Canada
Baumgartner, Christian
ETH Zürich, Switzerland
Dalca, Adrian
Massachusetts Institute of Technology, Harvard Medical School, Cambridge, USA
Sudre, Carole H.
University College London, UK
Wells, William M.
Harvard Medical School, Boston, USA
Drechsler, Klaus
Aachen University of Applied Sciences, Germany
Linguraru, Marius George
Children's National Health System
Oyarzun Laura, Cristina  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Shekhar, Raj
Sheikh Zayed Institute for Pediatric Surgical Innovation
Wesarg, Stefan  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
González Ballester, Miguel Angel
ICREA, Spain
Publisher
Springer Nature  
Publishing Place
Cham
Conference
International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) 2019  
International Workshop on Clinical Image-Based Procedures (CLIP) 2019  
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019  
DOI
10.1007/978-3-030-32689-0
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Singapore  
Keyword(s)
  • artificial intelligence (AI)

  • image processing

  • image segmentation

  • medical imaging

  • neural network

  • Lead Topic: Individual Health

  • Research Line: Computer graphics (CG)

  • Research Line: Computer vision (CV)

  • Research Line: Human computer interaction (HCI)

  • Research Line: (Interactive) simulation (SIM)

  • Research Line: Modeling (MOD)

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