An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index

S. Lakshmi; Deepu Vijayasenan; David S. Sumam; Saraswathy Sreeram; Pooja K. Suresh

doi:10.1109/TENCON.2019.8929640

An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index

S. Lakshmi, Deepu Vijayasenan, David S. Sumam, Saraswathy Sreeram, Pooja K. Suresh

Department of Pathology, Kasturba Medical College, Mangalore

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Ki-67 labeling index is a widely used biomarker for the diagnosis and monitoring of cancer. Many automated techniques have been proposed for evaluating Ki-67 index. In this paper, we introduce an integrated deep learning based approach. We use MobileUnet model for segmentation and classification and connected component based algorithm for the estimation of Ki-67 index in bladder cancer cases. The average F1 score is 0.92 and dice score is 0.96. The mean absolute error in the evaluated Ki-67 index is 2.1. We also explore possible pre-processing steps to generalize the segmentation model to at least one another type of cancer. Histogram matching and re-sizing improve the performance in breast cancer data by 12% in F1 score and 8% in dice score.

Original language	English
Title of host publication	Proceedings of the TENCON 2019
Subtitle of host publication	Technology, Knowledge, and Society
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2310-2314
Number of pages	5
ISBN (Electronic)	9781728118956
DOIs	https://doi.org/10.1109/TENCON.2019.8929640
Publication status	Published - 10-2019
Event	2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019 - Kerala, India Duration: 17-10-2019 → 20-10-2019

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2019-October
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019
Country/Territory	India
City	Kerala
Period	17-10-19 → 20-10-19

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/TENCON.2019.8929640

Cite this

Lakshmi, S., Vijayasenan, D., Sumam, D. S., Sreeram, S., & Suresh, P. K. (2019). An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index. In Proceedings of the TENCON 2019: Technology, Knowledge, and Society (pp. 2310-2314). Article 8929640 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2019.8929640

@inproceedings{1b4f79364cfa4acfa4366899f8a6739e,

title = "An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index",

abstract = "Ki-67 labeling index is a widely used biomarker for the diagnosis and monitoring of cancer. Many automated techniques have been proposed for evaluating Ki-67 index. In this paper, we introduce an integrated deep learning based approach. We use MobileUnet model for segmentation and classification and connected component based algorithm for the estimation of Ki-67 index in bladder cancer cases. The average F1 score is 0.92 and dice score is 0.96. The mean absolute error in the evaluated Ki-67 index is 2.1. We also explore possible pre-processing steps to generalize the segmentation model to at least one another type of cancer. Histogram matching and re-sizing improve the performance in breast cancer data by 12% in F1 score and 8% in dice score.",

author = "S. Lakshmi and Deepu Vijayasenan and Sumam, {David S.} and Saraswathy Sreeram and Suresh, {Pooja K.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019 ; Conference date: 17-10-2019 Through 20-10-2019",

year = "2019",

month = oct,

doi = "10.1109/TENCON.2019.8929640",

language = "English",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2310--2314",

booktitle = "Proceedings of the TENCON 2019",

address = "United States",

}

Lakshmi, S, Vijayasenan, D, Sumam, DS, Sreeram, S & Suresh, PK 2019, An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index. in Proceedings of the TENCON 2019: Technology, Knowledge, and Society., 8929640, IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2019-October, Institute of Electrical and Electronics Engineers Inc., pp. 2310-2314, 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019, Kerala, India, 17-10-19. https://doi.org/10.1109/TENCON.2019.8929640

An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index. / Lakshmi, S.; Vijayasenan, Deepu; Sumam, David S. et al.
Proceedings of the TENCON 2019: Technology, Knowledge, and Society. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2310-2314 8929640 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2019-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index

AU - Lakshmi, S.

AU - Vijayasenan, Deepu

AU - Sumam, David S.

AU - Sreeram, Saraswathy

AU - Suresh, Pooja K.

PY - 2019/10

Y1 - 2019/10

N2 - Ki-67 labeling index is a widely used biomarker for the diagnosis and monitoring of cancer. Many automated techniques have been proposed for evaluating Ki-67 index. In this paper, we introduce an integrated deep learning based approach. We use MobileUnet model for segmentation and classification and connected component based algorithm for the estimation of Ki-67 index in bladder cancer cases. The average F1 score is 0.92 and dice score is 0.96. The mean absolute error in the evaluated Ki-67 index is 2.1. We also explore possible pre-processing steps to generalize the segmentation model to at least one another type of cancer. Histogram matching and re-sizing improve the performance in breast cancer data by 12% in F1 score and 8% in dice score.

AB - Ki-67 labeling index is a widely used biomarker for the diagnosis and monitoring of cancer. Many automated techniques have been proposed for evaluating Ki-67 index. In this paper, we introduce an integrated deep learning based approach. We use MobileUnet model for segmentation and classification and connected component based algorithm for the estimation of Ki-67 index in bladder cancer cases. The average F1 score is 0.92 and dice score is 0.96. The mean absolute error in the evaluated Ki-67 index is 2.1. We also explore possible pre-processing steps to generalize the segmentation model to at least one another type of cancer. Histogram matching and re-sizing improve the performance in breast cancer data by 12% in F1 score and 8% in dice score.

UR - http://www.scopus.com/inward/record.url?scp=85077723602&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85077723602&partnerID=8YFLogxK

U2 - 10.1109/TENCON.2019.8929640

DO - 10.1109/TENCON.2019.8929640

M3 - Conference contribution

AN - SCOPUS:85077723602

T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON

SP - 2310

EP - 2314

BT - Proceedings of the TENCON 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019

Y2 - 17 October 2019 through 20 October 2019

ER -

Lakshmi S, Vijayasenan D, Sumam DS, Sreeram S , Suresh PK. An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index. In Proceedings of the TENCON 2019: Technology, Knowledge, and Society. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2310-2314. 8929640. (IEEE Region 10 Annual International Conference, Proceedings/TENCON). doi: 10.1109/TENCON.2019.8929640

An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this