ABI Workshop

Advances in Breast Imaging (cancelled, due to insufficient number of submissions)

Workshop website: http://users.aber.ac.uk/hgs08/ABI2015/

Call for papers: http://users.aber.ac.uk/hgs08/ABI2015/

Paper submission details: http://users.aber.ac.uk/hgs08/ABI2015/

Workshop chairs:

  • Harry Strange, Aberystwyth University, UK
  • Reyer Zwiggelaar, Aberystwyth University, UK
  • Moi Hoon Yap, Manchester Metropolitan University, UK

Breast cancer is the most common cancer affect- ing women worldwide causing 1 in 6 cancer related deaths in European women. Although incidence statistics remain high, mortality rates are dropping, thanks in part to early detection methods such as mammographic screening. Computer aided meth- ods for diagnosis and detection can play a key role in the diagnosis pathway and can help radiologists obtain a faster and more accurate diagnosis.

This workshop seeks to provide a platform for current and recent research within the field of breast imaging. In particular, we would welcome work with a specific focus on employing novel and in- teresting computer vision and machine learning to the problem domain of breast image analysis.

Work considered is not restricted to a single imaging modality, submissions are welcome that cover:

  • Digital mammography
  • Digital breast tomosynthesis
  • Digital pathology
  • MRI
  • CT
  • Ultrasound
  • Aesthetic evaluation of treatments

The workshop is expected to cover topics relating to technical advances in breast imaging including, but not limited to:

  • Image processing and reconstruction
  • Image segmentation
  • Image registration
  • Image quality assessment
  • Computer-aided diagnosis (CAD)
  • Computer-aided detection (CADx)
  • Multi-modal imaging
  • Visualisation
  • Large scale learning and analysis
  • Incorporation of semantic and clinical informa- tion
  • Phantom image generation

Keynote Speaker: The workshop will have two invited speakers, one of whom will discuss the challenges presented by breast imaging research from a clinical perspective and one of whom will discuss the challenges from a computer vision/machine learning perspective.

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