Context-sensitive patch histograms for detecting rare events in histopathological data - Diaz, Kristians; Baust, Maximilian; Navab, Nassir (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2017)
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.
CRIS-Team Scopus Importer:2023-03-08
Keywords: Histological image; Patch-based analysis; Unsupervised learning