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https://idr.l2.nitk.ac.in/jspui/handle/123456789/14705
Title: | Deep Learning Model based Ki-67 Index estimation with Automatically Labelled Data |
Authors: | Lakshmi S. Sai Ritwik K.V. Vijayasenan D. Sumam David S. Sreeram S. Suresh P.K. |
Issue Date: | 2020 |
Citation: | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS , Vol. 2020-July , , p. 1412 - 1415 |
Abstract: | Ki-67 labelling index is a biomarker which is used across the world to predict the aggressiveness of cancer. To compute the Ki-67 index, pathologists normally count the tumour nuclei from the slide images manually; hence it is timeconsuming and is subject to inter pathologist variability. With the development of image processing and machine learning, many methods have been introduced for automatic Ki-67 estimation. But most of them require manual annotations and are restricted to one type of cancer. In this work, we propose a pooled Otsu's method to generate labels and train a semantic segmentation deep neural network (DNN). The output is postprocessed to find the Ki-67 index. Evaluation of two different types of cancer (bladder and breast cancer) results in a mean absolute error of 3.52%. The performance of the DNN trained with automatic labels is better than DNN trained with ground truth by an absolute value of 1.25%. © 2020 IEEE. |
URI: | https://doi.org/10.1109/EMBC44109.2020.9175752 http://idr.nitk.ac.in/jspui/handle/123456789/14705 |
Appears in Collections: | 2. Conference Papers |
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