by Xavier Llorà, Anusha Priya, and Rohit Bhargava (2006).
To appear in the Special Issue on Learning Classifier Systems of the Natural Computing Journal. Also as IlliGAL TR No. 2006027. Link to the PDF.
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 NAX solving the prostate tissue type prediction and how such and efficient implementation makes it a very powerful tool for biomedical image processing.