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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2014
Subtitle of host publicationDigital Pathology
PublisherSPIE
ISBN (Print)9780819498342
DOIs
StatePublished - Jan 1 2014
EventMedical Imaging 2014: Digital Pathology - San Diego, CA, United States
Duration: Feb 16 2014Feb 17 2014

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9041
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2014: Digital Pathology
CountryUnited States
CitySan Diego, CA
Period2/16/142/17/14

Fingerprint

breast
Breast
cancer
Tissue
Infrared radiation
Imaging techniques
Biopsy
Tumors
Neoplasms
Coloring Agents
staining
Quality Control
tumors
Immunohistochemistry
Staining and Labeling
Breast Neoplasms
Research
Pathologists
Therapeutics

Keywords

  • breast
  • cancer
  • classification
  • immunohistochemistry
  • mid-infrared
  • random forests
  • spectroscopy
  • tissue micro-array

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Mayerich, D. M., Walsh, M., Kadjacsy-Balla, A., Mittal, S., & Bhargava, R. (2014). Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data. In Medical Imaging 2014: Digital Pathology [904107] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9041). SPIE. https://doi.org/10.1117/12.2043783

Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data. / Mayerich, David M.; Walsh, Michael; Kadjacsy-Balla, Andre; Mittal, Shachi; Bhargava, Rohit.

Medical Imaging 2014: Digital Pathology. SPIE, 2014. 904107 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9041).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mayerich, DM, Walsh, M, Kadjacsy-Balla, A, Mittal, S & Bhargava, R 2014, Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data. in Medical Imaging 2014: Digital Pathology., 904107, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9041, SPIE, Medical Imaging 2014: Digital Pathology, San Diego, CA, United States, 2/16/14. https://doi.org/10.1117/12.2043783
Mayerich DM, Walsh M, Kadjacsy-Balla A, Mittal S, Bhargava R. Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data. In Medical Imaging 2014: Digital Pathology. SPIE. 2014. 904107. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2043783
Mayerich, David M. ; Walsh, Michael ; Kadjacsy-Balla, Andre ; Mittal, Shachi ; Bhargava, Rohit. / Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data. Medical Imaging 2014: Digital Pathology. SPIE, 2014. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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