20 March 2014 Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data
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Abstract
Current methods for cancer detection rely on clinical stains, often using immunohistochemistry techniques. Pathologists then evaluate the stained tissue in order to determine cancer stage treatment options. These methods are commonly used, however they are non-quantitative and it is difficult to control for staining quality. In this paper, we propose the use of mid-infrared spectroscopic imaging to classify tissue types in tumor biopsy samples. Our goal is to augment the data available to pathologists by providing them with quantitative chemical information to aid diagnostic activities in clinical and research activities related to breast cancer.
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David M. Mayerich, David M. Mayerich, Michael Walsh, Michael Walsh, Andre Kadjacsy-Balla, Andre Kadjacsy-Balla, Shachi Mittal, Shachi Mittal, Rohit Bhargava, Rohit Bhargava, } "Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data", Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 904107 (20 March 2014); doi: 10.1117/12.2043783; https://doi.org/10.1117/12.2043783
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