Illinois Genetic Algorithms Laboratory

Xavier Llorà, Anusha Priya, and Rohit Bhargava (2006)
TR No.: 2006027 | Download PDF | Download PS

Abstract:
Prostate cancer accounts for one-third of noncutaneous cancers
diagnosed in US men, and it is a leading cause of cancer-related
death. Advances in Fourier transform infrared spectroscopy of stained
tissue is now able to provide very large data sets describing the
chemical properties of the cells forming the prostate tissue. Uniting
spectroscopic imaging data and computer-aided diagnoses (CADx), we
seek to provide a new approach to pathology by automating the
recognition of cancer in complex tissue. The first step toward the
creation of such CADx tools requires mechanisms for automatically
learn tissue type classification—a key step on the diagnosis
process. As we will show, genetics-based machine learning (GBML) can
be used to approach such a problem. However, there is an urge for
efficient and scalable implementations that enable to process such
very large data sets. This paper proposes and validates and efficient
GBML technique—NAX—based on an incremental
genetics-based rule learner that exploits massive parallelisms—via
the message passing interface (MPI)—and efficient rule-matching
using hardware-implemented operations. Results show the competence of
{\tt NAX} solving the prostate tissue type prediction and how such
and efficient implementation makes it a very powerful tool for
biomedical image processing.