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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, January 27, 2004}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Max Diem}
\centerline{\sl City University of New York}
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\centerline{\bf Multivariate Analysis of Infrared Spectral Maps to Detect}
\centerline{\bf Cancer in Tissue Biopsies}
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Abstract:

Infrared (IR) Spectroscopy is a well-established analytical method, in
which infrared radiation causes molecules to access higher levels of
vibrational excitation. In this process, distinct infrared ˇ°spectraˇ± are
produced for all molecular species.

Since IR spectra can be collected for as little as 10-10 gram of sample,
it can be used to monitor the chemical composition of individual human
cells, and compositional changes that may be caused by disease. In
infrared spectral imaging, thousands of infrared spectra are collected
for individual pixels of tissue, quite similar to digital imaging first
introduced during LANDSAT observations. However, rather than imaging at
a few discrete colors, in IR imaging the entire, continuous spectra are
sampled.

Multivariate methods, in particular Principal Component Analysis (PCA)
and Hierarchical Cluster Analysis (HCA) have been used to extract small,
but reproducible spectral changes, and to obtain pseudo-color tissue
images in a totally unsupervised fashion. Further developments include
the use of trained, diagnostic algorithms, such as artificial neural
nets, to obtain stable, reliable methods of medical diagnoses based on
objective measurements and mathematical analysis.

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