From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
Markers such as CD13 and CD133 have been used to identify Cancer Stem Cells (CSC) in various tissue images. It is highly likely that CSC nuclei appear as brown in CD13 stained liver tissue images. We observe that there is a high correlation between the ratio of brown to blue colored nuclei in CD13 images and the ratio between the dark blue to blue colored nuclei in H&E stained liver images. Therefore, we recommend that a pathologist observing many dark blue nuclei in an H&E stained tissue image may also order CD13 staining to estimate the CSC ratio. In this paper, we describe a computer vision method based on a neural network estimating the ratio of dark blue to blue colored nuclei in an H&E stained liver tissue image. The neural network structure is based on a multiplication free operator using only additions and sign operations. Experimental results are presented.
Diaa Badawi, Ece Akhan, Ma'en Mallah, Ayşegül Üner, Rengül Çetin-Atalay, and A. Enis Çetin, "Multiplication free neural network for cancer stem cell detection in H-and-E stained liver images," Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 102110C (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 12, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2262338.
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Study of self-shadowing effect as a simple means to realize nanostructured thin films and layers with special attentions to birefringent obliquely deposited thin films and photo-luminescent porous silicon