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Title: An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index
Authors: Lakshmi, S.
Vijayasenan, D.
Sumam, David S.
Sreeram, S.
Suresh, P.K.
Issue Date: 2019
Citation: IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019, Vol.2019-October, , pp.2310-2314
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.
Appears in Collections:2. Conference Papers

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