Abstract
Grading of tissue based on microscopic images is a common and challenging task. We propose a new method for grading of wholeslide histology images of invasive breast carcinoma, which is based on mitotic cell detection. The method combines a threshold-based attention mechanism and a deep neural network for mitotic cell detection and grading. Our mitotic cell detector is learned from scratch using object centroids. We achieved competitive results in the recent MICCAI TUPAC16 challenge.
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© 2017 Springer-Verlag GmbH Deutschland
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Wollmann, T., Rohr, K. (2017). Automatic Grading of Breast Cancer Whole-Slide Histopathology Images. In: Maier-Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_56
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DOI: https://doi.org/10.1007/978-3-662-54345-0_56
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Publisher Name: Springer Vieweg, Berlin, Heidelberg
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Online ISBN: 978-3-662-54345-0
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