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 language | English (US) |
|---|---|
| Title of host publication | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 620-623 |
| Number of pages | 4 |
| Volume | 2018-July |
| ISBN (Electronic) | 9781538636466 |
| DOIs | |
| State | Published - Oct 26 2018 |
| Event | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States Duration: Jul 18 2018 → Jul 21 2018 |
Other
| Other | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 |
|---|---|
| Country | United States |
| City | Honolulu |
| Period | 7/18/18 → 7/21/18 |
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ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Deep Learning Models Differentiate Tumor Grades from HE Stained Histology Sections
AU - Khoshdeli, Mina
AU - Borowsky, Alexander D
AU - Parvin, Bahram
PY - 2018/10/26
Y1 - 2018/10/26
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85056650427&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8512357
DO - 10.1109/EMBC.2018.8512357
M3 - Conference contribution
VL - 2018-July
SP - 620
EP - 623
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -