A Guided Spatial Transformer Network for Histology Cell Differentiation

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dc.contributor.authorAubreville, Marcen_US
dc.contributor.authorKrappmann, Maximilianen_US
dc.contributor.authorBertram, Christofen_US
dc.contributor.authorKlopfleisch, Roberten_US
dc.contributor.authorMaier, Andreasen_US
dc.contributor.editorStefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Riederen_US
dc.date.accessioned2017-09-06T07:12:24Z
dc.date.available2017-09-06T07:12:24Z
dc.date.issued2017
dc.description.abstractIdentification 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.en_US
dc.description.sectionheadersBiology and Networks
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20171233
dc.identifier.isbn978-3-03868-036-9
dc.identifier.issn2070-5786
dc.identifier.pages21-25
dc.identifier.urihttps://doi.org/10.2312/vcbm.20171233
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20171233
dc.publisherThe Eurographics Associationen_US
dc.subjectCCS Concepts
dc.subjectComputing methodologies
dc.subjectObject detection
dc.subjectNeural networks
dc.subjectApplied computing
dc.subjectBioinformatics
dc.titleA Guided Spatial Transformer Network for Histology Cell Differentiationen_US
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VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine