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Herausgeber: 
  • Kenji Suzuki
  • Dinggang Shen
  • Fei Wang
  • Pingkun Yan
  • Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  September 2010  
    Genre:  EDV / Informatik 
     
    algorithmanalysisandproblemcomplexity / Algorithmen und Datenstrukturen / Algorithmus / Anatomy / Bildgebende Verfahren / Bildverarbeitung / Computer-AidedDiagnosis / Data Mining (EDV)
    ISBN:  9783642159473 
    EAN-Code: 
    9783642159473 
    Verlag:  Springer 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Image Processing, Computer Vision, Pattern Recognition, and Graphics
    #6357 - Lecture Notes in Computer Science  
    Dimensionen:  H 235 mm / B 155 mm / D 12 mm 
    Gewicht:  318 gr 
    Seiten:  204 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    The first International Workshop on Machine Learning in Medical Imaging, MLMI 2010, was held at the China National Convention Center, Beijing, China on Sept- ber 20, 2010 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2010. Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, ima- guided therapy, image annotation, and image database retrieval. With advances in me- cal imaging, new imaging modalities, and methodologies such as cone-beam/multi-slice CT, 3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical impedance to- graphy, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Single-sample evidence provided by the patient¿s imaging data is often not sufficient to provide satisfactory performance; the- fore tasks in medical imaging require learning from examples to simulate a physician¿s prior knowledge of the data. The MLMI 2010 is the first workshop on this topic. The workshop focuses on major trends and challenges in this area, and works to identify new techniques and their use in medical imaging. Our goal is to help advance the scientific research within the broad field of medical imaging and machine learning. The range and level of submission for this year's meeting was of very high quality. Authors were asked to submit full-length papers for review. A total of 38 papers were submitted to the workshop in response to the call for papers.

      



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