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

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

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 languageEnglish
Title of host publicationProceedings of the TENCON 2019
Subtitle of host publicationTechnology, Knowledge, and Society
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2310-2314
Number of pages5
ISBN (Electronic)9781728118956
DOIs
Publication statusPublished - 10-2019
Event2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019 - Kerala, India
Duration: 17-10-201920-10-2019

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2019-October
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019
CountryIndia
CityKerala
Period17-10-1920-10-19

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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). [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