Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/7319
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dc.contributor.authorLakshmi, S.-
dc.contributor.authorVijayasenan, D.-
dc.contributor.authorSumam, David S.-
dc.contributor.authorSreeram, S.-
dc.contributor.authorSuresh, P.K.-
dc.date.accessioned2020-03-30T09:58:50Z-
dc.date.available2020-03-30T09:58:50Z-
dc.date.issued2019-
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019, Vol.2019-October, , pp.2310-2314en_US
dc.identifier.urihttps://idr.nitk.ac.in/jspui/handle/123456789/7319-
dc.description.abstractKi-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.en_US
dc.titleAn Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Indexen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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