Abstract
Advances in digital health records, computing and machine learning have led to the synergistic rise of machine learning techniques being applied to medical imaging tasks such as detection, diagnosis and discovery. Recent advances in computer vision and image processing have been applied to medical imaging yielding vast performance improvements over existing methods. Breast cancer is a leading cause of death among cancer patients in women. Mitotic count in biopsied breast tissue is an important biomarker for predicting breast cancer prognosis as per the Nottingham Grading System. In this work, we survey different deep learning based approaches to detect mitotic cells with the overall aim of assisting pathologists with the diagnosis of breast cancer.
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Kaushik, S., Raghavan, S.V., Sivaselvan, B. (2019). A Study of Deep Learning Methods for Mitotic Cell Detection Towards Breast Cancer Diagnosis. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-13-9939-8_23
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