Abstract
Scoring the size/shape variations of cancer nuclei (nuclear pleomorphism) in breast cancer histopathology images is a critical prog-nostic marker in breast cancer grading and has been subject to a con-siderable amount of observer variability and subjectivity issues. In spite of a decade long histopathology image analysis research, automated as-sessment of nuclear pleomorphism remains challenging due to the com-plex visual appearance and huge variability of cancer nuclei.This study proposes a practical application of the deep belief based deep neural net-work (DBN-DNN) model to determine the nuclear pleomorphism score of breast cancer tissue. The DBN-DNN network is trained to classify a breast cancer histology image into one of the three groups: score 1, score 2 and score 3 nuclear pleomorphism by learning the mean and standard deviation of morphological and texture features of the entire nuclei population contained in a breast histology image. The model was trained for features from automatically-segmented nuclei from 80 breast cancer histopathology images selected from publicly available MITOS-ATYPIA dataset. The classification accuracy of the model on the training and testing datasets was found to be 96 % and 90 % respectively.
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Acknowledgement
The first two authors would like to acknowledge the Department of Science and Technology (DST), Govt. of India for providing computing facility at the institution through the FIST programme. The authors would also like to thank the organizers of the MITOS-ATYPIA 2014 contest for their consent to use images from their dataset.
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Maqlin, P., Thamburaj, R., Mammen, J.J., Manipadam, M.T. (2015). Automated Nuclear Pleomorphism Scoring in Breast Cancer Histopathology Images Using Deep Neural Networks. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_26
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DOI: https://doi.org/10.1007/978-3-319-26832-3_26
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