An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index
| dc.contributor.author | Lakshmi, S. | |
| dc.contributor.author | Vijayasenan, D. | |
| dc.contributor.author | Sumam David, S. | |
| dc.contributor.author | Sreeram, S. | |
| dc.contributor.author | Suresh, P.K. | |
| dc.date.accessioned | 2026-02-06T06:37:16Z | |
| dc.date.issued | 2019 | |
| dc.description.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. © 2019 IEEE. | |
| dc.identifier.citation | IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019, Vol.2019-October, , p. 2310-2314 | |
| dc.identifier.issn | 21593442 | |
| dc.identifier.uri | https://doi.org/10.1109/TENCON.2019.8929640 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/30969 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | Carcinoma bladder | |
| dc.subject | Deep Neural Network | |
| dc.subject | Ki-67 index | |
| dc.title | An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index |