Aubreville M, Krappmann M, Bertram C, Klopfleisch R, Maier A (2017)
Publication Type: Conference contribution, Original article
Publication year: 2017
Publisher: Eurographics Digital Library
Pages Range: 021-025
Conference Proceedings Title: Eurographics Workshop on Visual Computing for Biology and Medicine
ISBN: 978-3-03868-036-9
URI: https://arxiv.org/pdf/1707.08525.pdf
Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45 %. In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologist.
APA:
Aubreville, M., Krappmann, M., Bertram, C., Klopfleisch, R., & Maier, A. (2017). A Guided Spatial Transformer Network for Histology Cell Differentiation. In Stefan Bruckner, Anja Hennemuth, Bernhard Kainz, Ingrid Hotz, Dorit Merhof and Christian Rieder (Eds.), Eurographics Workshop on Visual Computing for Biology and Medicine (pp. 021-025). Bremen, DE: Eurographics Digital Library.
MLA:
Aubreville, Marc, et al. "A Guided Spatial Transformer Network for Histology Cell Differentiation." Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine, Bremen Ed. Stefan Bruckner, Anja Hennemuth, Bernhard Kainz, Ingrid Hotz, Dorit Merhof and Christian Rieder, Eurographics Digital Library, 2017. 021-025.
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