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.

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