Lakshmi S.Sai Ritwik K.V.Vijayasenan D.Sumam David S.Sreeram S.Suresh P.K.2021-05-052021-05-052020Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS , Vol. 2020-July , , p. 1412 - 1415https://doi.org/10.1109/EMBC44109.2020.9175752https://idr.nitk.ac.in/handle/123456789/14705Ki-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.Conference Paper