An Advanced Probabilistic Framework for Assisting Screening Mammogram Interpretation - Computational Intelligence in Healthcare: Advanced Methodologies - page 356

Chapter 17
An Advanced Probabilistic Framework for
Assisting Screening Mammogram Interpretation

Marina Velikova 1 , , Nivea Ferreira 1 ,MauriceSamulski 2 ,
Peter J.F. Lucas 1 , and Nico Karssemeijer 2

1 Institute for Computing and Information Sciences, Radboud University Nijmegen
6525 ED, Nijmegen, The Netherlands
2 Department of Radiology, Radboud University Nijmegen Medical Centre
6525 GA, Nijmegen, The Netherlands

Abstract. Breast cancer is the most common form of cancer among
women world-wide. One in nine women will be diagnosed with a form of
breast cancer in her lifetime. In an effort to diagnose cancer at an early
stage, screening programs have been introduced by using periodic mam-
mographic examinations in asymptomatic women. In evaluating screen-
ing cases, radiologists are usually presented with two mammographic
images of each breast as a cancerous lesion tends to be observed in dif-
ferent breast projections (views). Most computer-aided detection (CAD)
systems, on the other hand, only analyse single views independently,
and thus fail to account for the interaction between the views and the
breast cancer detection can be obscured due to the lack of consistency
in lesion marking. This limits the usability and the trust in the perfor-
mance of such systems. In this chapter, we propose a unified Bayesian
network framework for exploiting multi-view dependencies between the
suspicious regions detected by a single-view CAD system. The framework
is based on a multi-stage scheme, which models the way radiologists in-
terpret mammograms, at four different levels: region, view, breast and
case. At each level, we combine all available image information for the
patient obtained from a single-view CAD system using a special class of
Bayesian networks-causal independence models. The results from exper-
iments with actual screening data of 1063 cases, from which 383 were
cancerous, show that our approach outperforms the single-view CAD
system in distinguishing between normal and abnormal cases. This is
a promising step towards the development of automated systems that
can provide a valuable “second opinion” to the screening radiologists for
improved evaluation of breast cancer cases.

1 Introduction

According to the International Agency for Research on Cancer (IARC), breast
cancer is the most common form of cancer among women world-wide, and evi-
dence show that early detection combined to appropriate treatment is currently
Corresponding author: [e-mail]