Virtual Microscopy and Grid-enabled Decision Support for Large Scale Analysis of Imaged Pathology Specimens

By Lin F. Yang, Wenjin Chen, Peter Meer, Gratian Salaru, Lauri A. Goodell, Viktors Berstis, David J. Foran. 2009

42 citations recorded by the source corpus.

Breast cancer accounts for about 30% of all cancers and 15% of cancer deaths in women. Advances in computer-assisted analysis hold promise for classifying subtypes of disease and improving prognostic accuracy. We introduce a grid-enabled decision support system for performing automatic analysis of imaged breast tissue microarrays. To date, we have processed more than 1 00 000 digitized specimens (1200 times 1200 pixels each) on IBM's World Community Grid (WCG). As a part of the Help Defeat Cancer (HDC) project, we have analyzed that the data returned from WCG along with retrospective patient clinical profiles for a subset of 3744 breast tissue samples, and have reported the results in this paper. Texture-based features were extracted from the digitized specimens, and isometric feature mapping was applied to achieve nonlinear dimension reduction. Iterative prototyping and testing were performed to classify several major subtypes of breast cancer. Overall, the most reliable approach was gentle AdaBoost using an eight-node classification and regression tree as the weak learner. Using the proposed algorithm, a binary classification accuracy of 89% and the multiclass accuracy of 80% were achieved. Throughout the course of the experiments, only 30% of the dataset was used for training.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Biophysics

In Computer Vision and Pattern Recognition

Citations in the corpus

Multi-class texture analysis in colorectal cancer histology (2016, methodology): “Lastly, another method applied to breast cancer samples has been reported to achieve 89% accuracy for three tissue categories 43 .”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “In order to handle large-scale image datasets, grid computing [9]–[11] and computationally scalable algorithms”
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles (2016, background): “…al., 1997; Sertel et al., 2010; Sparks and Madabhushi, 2013; Sudbø et al., 2000; Tabesh and Teverovskiy, 2006; Tabesh et al., 2007, 2005; Tahir and Bouridane, 2006; Tasoulis et al., 2003; Teverovskiy et al., 2004; Thiran and Macq, 1996; Tsai et al., 2009; Xu et al. 2014a, 2014b; Yang et al., 2009).”