Browsing by Author "Lakshmi, S."

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    An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index
    (Institute of Electrical and Electronics Engineers Inc., 2019) Lakshmi, S.; Vijayasenan, D.; Sumam David, S.; Sreeram, S.; Suresh, P.K.
    Ki-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.
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    Deep Learning Model based Ki-67 Index estimation with Automatically Labelled Data
    (Institute of Electrical and Electronics Engineers Inc., 2020) Lakshmi, S.; Sai Ritwik, K.V.; Vijayasenan, D.; Sumam David, S.; Sreeram, S.; Suresh, P.K.
    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.
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    An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index
    (2019) Lakshmi, S.; Vijayasenan, D.; Sumam, David S.; Sreeram, S.; Suresh, P.K.
    Ki-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.