Please use this identifier to cite or link to this item: http://idr.nitk.ac.in/jspui/handle/123456789/14705
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dc.contributor.authorLakshmi S.
dc.contributor.authorSai Ritwik K.V.
dc.contributor.authorVijayasenan D.
dc.contributor.authorSumam David S.
dc.contributor.authorSreeram S.
dc.contributor.authorSuresh P.K.
dc.date.accessioned2021-05-05T10:15:41Z-
dc.date.available2021-05-05T10:15:41Z-
dc.date.issued2020
dc.identifier.citationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS , Vol. 2020-July , , p. 1412 - 1415en_US
dc.identifier.urihttps://doi.org/10.1109/EMBC44109.2020.9175752
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14705-
dc.description.abstractKi-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.titleDeep Learning Model based Ki-67 Index estimation with Automatically Labelled Dataen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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