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An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index
(Institute of Electrical and Electronics Engineers Inc., 2019) Lakshmi, S.; Vijayasenan, D.; Sumam David, S.; Sreeram, S.; Suresh, P.K.
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. Â© 2019 IEEE.