Deep Learning Models Differentiate Tumor Grades from HE Stained Histology Sections

Mina Khoshdeli, Alexander D Borowsky, Bahram Parvin

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

1 Citation (Scopus)

Abstract

Aberration in tissue architecture is an essential index for cancer diagnosis and tumor grading. Therefore, extracting features of aberrant phenotypes and classification of the histology tissue can provide a model for computer-aided pathology (CAP). As a case study, we investigate the application of convolutional neural networks (CNN)s for tumor grading and decomposing tumor architecture from hematoxylin and eosin (HE) stained histology sections of kidney. The former and latter contribute to CAP and the role of the tumor architecture on the outcome (e.g., survival), respectively. A training set is constructed and sample images are classified into six categories of normal, fat, blood, stroma, low-grade granular tumor, and high-grade clear cell carcinoma. We have compared the performances of a deep versus shallow networks, and shown that the deeper model outperforms the shallow network.

Original languageEnglish (US)
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages620-623
Number of pages4
Volume2018-July
ISBN (Electronic)9781538636466
DOIs
StatePublished - Oct 26 2018
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: Jul 18 2018Jul 21 2018

Other

Other40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period7/18/187/21/18

Fingerprint

Histology
Hematoxylin
Eosine Yellowish-(YS)
Tumors
Learning
Neoplasm Grading
Pathology
Neoplasms
Tissue
Computer Simulation
Oils and fats
Aberrations
Fats
Carcinoma
Phenotype
Kidney
Blood
Cells
Deep learning
Neural networks

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Khoshdeli, M., Borowsky, A. D., & Parvin, B. (2018). Deep Learning Models Differentiate Tumor Grades from HE Stained Histology Sections. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (Vol. 2018-July, pp. 620-623). [8512357] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2018.8512357

Deep Learning Models Differentiate Tumor Grades from HE Stained Histology Sections. / Khoshdeli, Mina; Borowsky, Alexander D; Parvin, Bahram.

40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July Institute of Electrical and Electronics Engineers Inc., 2018. p. 620-623 8512357.

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

Khoshdeli, M, Borowsky, AD & Parvin, B 2018, Deep Learning Models Differentiate Tumor Grades from HE Stained Histology Sections. in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. vol. 2018-July, 8512357, Institute of Electrical and Electronics Engineers Inc., pp. 620-623, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, United States, 7/18/18. https://doi.org/10.1109/EMBC.2018.8512357
Khoshdeli M, Borowsky AD, Parvin B. Deep Learning Models Differentiate Tumor Grades from HE Stained Histology Sections. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July. Institute of Electrical and Electronics Engineers Inc. 2018. p. 620-623. 8512357 https://doi.org/10.1109/EMBC.2018.8512357
Khoshdeli, Mina ; Borowsky, Alexander D ; Parvin, Bahram. / Deep Learning Models Differentiate Tumor Grades from HE Stained Histology Sections. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July Institute of Electrical and Electronics Engineers Inc., 2018. pp. 620-623
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