Assessment of histopathological data is not only difficult due to its varying appearance, e.g. caused by staining artifacts, but also due to its sheer size: Common whole slice images feature a resolution of 6000x4000 pixels. Therefore, finding rare events in such data sets is a challenging and tedious task and developing sophisticated computerized tools is not easy, especially when no or little training data is available. In this work, we propose learning-free yet effective approach based on context sensitive patch-histograms in order to find extramedullary hematopoiesis events in Hematoxylin-Eosin-stained images. When combined with a simple nucleus detector, one can achieve performance levels in terms of sensitivity 0.7146, specificity 0.8476 and accuracy 0.8353 which are very well comparable to a recently published approach based on random forests.
Kristians Diaz, Maximilian Baust, and Nassir Navab, "Context-sensitive patch histograms for detecting rare events in histopathological data," Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400F (Presented at SPIE Medical Imaging: February 13, 2017; Published: 1 March 2017); https://doi.org/10.1117/12.2254014.
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