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DC Field | Value | Language |
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dc.contributor.author | Lakshmi S. | |
dc.contributor.author | Sai Ritwik K.V. | |
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 | 2021-05-05T10:15:41Z | - |
dc.date.available | 2021-05-05T10:15:41Z | - |
dc.date.issued | 2020 | |
dc.identifier.citation | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS , Vol. 2020-July , , p. 1412 - 1415 | en_US |
dc.identifier.uri | https://doi.org/10.1109/EMBC44109.2020.9175752 | |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/14705 | - |
dc.description.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. | en_US |
dc.title | Deep Learning Model based Ki-67 Index estimation with Automatically Labelled Data | en_US |
dc.type | Conference Paper | en_US |
Appears in Collections: | 2. Conference Papers |
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