Context-sensitive patch histograms for detecting rare events in histopathological data
Diaz K, Baust M, Navab N (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: SPIE
Book Volume: 10140
Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Event location: Orlando, FL, USA
ISBN: 9781510607255
DOI: 10.1117/12.2254014
Abstract
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.
Involved external institutions
Germany (DE)How to cite
APA:
Diaz, K., Baust, M., & Navab, N. (2017). Context-sensitive patch histograms for detecting rare events in histopathological data. In Metin N. Gurcan, John E. Tomaszewski (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Orlando, FL, USA: SPIE.
MLA:
Diaz, Kristians, Maximilian Baust, and Nassir Navab. "Context-sensitive patch histograms for detecting rare events in histopathological data." Proceedings of the Medical Imaging 2017: Digital Pathology, Orlando, FL, USA Ed. Metin N. Gurcan, John E. Tomaszewski, SPIE, 2017.
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