Saturday, June 24, 2017 - 14:45 to 15:45
A comparative study of cell nuclei attributed relational graphs for knowledge description and categorization in histopathological gastric cancer whole slide images
In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification. A detailed comparative analysis of the proposed graph-based methods, also with the established low-level, object-level and high-level image descriptions is performed, that further leads to a hybrid approach combining salient visual information. Quantitative evaluation of investigated methods suggests the suitability of particular graph variants for automatic classification using H&E stained histopathological gastric cancer whole slide images based on HER2 immunohistochemistry.
Harshita Sharma's picture
Harshita Sharma
Technical University Berlin (DE)
Norman Zerbe's picture
Norman Zerbe
Christine BOger's picture
Christine BOger
Stephan Wienert's picture
Stephan Wienert
Olaf Hellwich's picture
Olaf Hellwich
Peter Hufnagl's picture
Peter Hufnagl