High throughput analysis of breast cancer specimens on the grid.
Lin Yang(1,2), Wenjin Chen(2), P. Meer(1),
G. Salaru(2), M.D. Feldman(3), D.J. Foran(2)_
(1)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
(2)BioImaging Laboratory, Department of Pathology and Laboratory Medicine
UMDNJ-Robert Wood Johnson Medical School
Piscataway, NJ 08855, USA
(3)Dept. of Surgical Pathology, Univ. of Pennsylvania, Philadelphia, PA
19104, USA
Breast cancer accounts for about 30% of all cancers and 15% of
all cancer deaths in women in the United States. Advances in
computer assisted diagnosis (CAD) holds promise for early detecting
and staging disease progression. In this paper we introduce a
Grid-enabled CAD to perform automatic analysis of imaged
histopathology breast tissue specimens. More than 100,000 digitized
samples (1200*1200 pixels) have already been processed on
the Grid. We have analyzed results for 3744 breast tissue samples,
which were originated from four different institutions using
diaminobenzidine (DAB) and hematoxylin staining. Both linear and
nonlinear dimension reduction techniques are compared, and the best
one (ISOMAP) was applied to reduce the dimensionality of the
features. The experimental results show that the Gentle Boosting
using an eight node CART decision tree as the weak learner provides
the best result for classification. The algorithm has an accuracy of
86.02% using only 20% of the specimens as the training set.
10th International Conference on Medical Image Computing and
Computer Assisted Intervention (MICCAI) ,
Brisbane, Australia, November 2007, Springer, LNCS 4791, 617-624.
