@inproceedings{f10dd852d7f441dab5c6babc39dd7982,
title = "Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data",
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.",
keywords = "breast, cancer, classification, immunohistochemistry, mid-infrared, random forests, spectroscopy, tissue micro-array",
author = "Mayerich, {David M.} and Michael Walsh and Andre Kadjacsy-Balla and Shachi Mittal and Rohit Bhargava",
year = "2014",
doi = "10.1117/12.2043783",
language = "English (US)",
isbn = "9780819498342",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
booktitle = "Medical Imaging 2014",
note = "Medical Imaging 2014: Digital Pathology ; Conference date: 16-02-2014 Through 17-02-2014",
}