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Automated Diagnosis of Breast Cancer on Medical Images

  • Marina VelikovaEmail author
  • Inês Dutra
  • Elizabeth S. Burnside
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9521)

Abstract

The development and use of computerized decision-support systems in the domain of breast cancer has the potential to facilitate the early detection of disease as well as spare healthy women unnecessary interventions. Despite encouraging trends, there is much room for improvement in the capabilities of such systems to further alleviate the burden of breast cancer. One of the main challenges that current systems face is integrating and translating multi-scale variables like patient risk factors and imaging features into complex management recommendations that would supplement and/or generalize similar activities provided by subspecialty-trained clinicians currently. In this chapter, we discuss the main types of knowledge—object-attribute, spatial, temporal and hierarchical—present in the domain of breast image analysis and their formal representation using two popular techniques from artificial intelligence—Bayesian networks and first-order logic. In particular, we demonstrate (i) the explicit representation of uncertain relationships between low-level image features and high-level image findings (e.g., mass, microcalcifications) by probability distributions in Bayesian networks, and (ii) the expressive power of logic to generally represent the dynamic number of objects in the domain. By concrete examples with patient data we show the practical application of both formalisms and their potential for use in decision-support systems.

Keywords

Breast Cancer Bayesian Network Knowledge Representation Breast Cancer Diagnosis First Order Logic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marina Velikova
    • 1
    Email author
  • Inês Dutra
    • 2
  • Elizabeth S. Burnside
    • 3
  1. 1.Embedded Systems Innovation by TNOEindhovenThe Netherlands
  2. 2.Departamento de Ciência de Computadores, Faculdade de CiênciasUniversidade do PortoPortoPortugal
  3. 3.Department of RadiologyUniversity of Wisconsin School of Medicine and Public HealthMadisonUSA

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