Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data

David M. Mayerich; Michael Walsh; Andre Kadjacsy-Balla; Shachi Mittal; Rohit Bhargava

doi:10.1117/12.2043783

20 March 2014 Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data

David M. Mayerich, Michael Walsh, Andre Kadjacsy-Balla, Shachi Mittal, Rohit Bhargava

Author Affiliations +

Proceedings Volume 9041, Medical Imaging 2014: Digital Pathology; 904107 (2014) https://doi.org/10.1117/12.2043783
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

David M. Mayerich, Michael Walsh, Andre Kadjacsy-Balla, Shachi Mittal, and 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); https://doi.org/10.1117/12.2043783

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