PathMiner: A Web Based Tool for Computer Assisted Diagnostics in Pathology.

Lin Yang(1,3), Oncel Tuzel(2), Wenjin Chen(3), Peter Meer(1), Gratian Salaru(4), Lauri A. Goodell(4), Adam Bagg(5) and David J. Foran(3)

(1)Department of Electrical and Computer Engineering
(2)Department of Computer Science
Rutgers University, Piscataway, NJ 08854, USA

(3)BioImaging Laboratory, Department of Pathology and Laboratory Medicine
UMDNJ-Robert Wood Johnson Medical School
Piscataway, NJ 08855, USA

(4) Cancer Institue of New Jersey, New Brunswick, NJ, 08903, USA

(5) Department of Pathology and Laboratory Medicine
University of Pennsylvania, Philadelphia, PA, 19104, USA

Large-scale, multi-site collaboration has become indispensable for a wide range of research and clinical activities which rely on the capacity of individuals to dynamically acquire, share and assess images and correlated data. In this paper, we introduce a web-based system, Pathminer, for interactive telemedicine, intelligent archiving and automated decision support in pathology. The PathMiner system supports network-based submission of queries and can automatically locate and retrieve digitized pathology specimens. It correlated molecular studies of cases from "ground-truth" databases which exhibit spectral and spatial profiles consistent with the query image. The statistically most probable diagnosis or structural classification is provided to the individual who is seeking decision support. To test the system under real-case scenarios a network-based laboratory has been established at strategic sites at UMDNJ - Robert Wood Johnson Medical School, Robert Wood Johnson University Hospital, the University of Pennsylvania School of Medicine, Hospital of the University of Pennsylvania, The Cancer Institute of New Jersey, and Rutgers University. The average five class classification accuracy of the system is 93.18% based on ten fold cross validation on a dataset containing 3691 images. We also show prospective performance of PathMiner in real application where the images exhibited large variances in staining characters compared with the training data. The average five class classification accuracy in this open set experiments is 87.22%.

IEEE Transactions on Information Technology in BioMedicine, 13 , 291-299, 2009.

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