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Moshe Safran, CEO, RSIP Vision USA
Abstract:
We present a comprehensive, neural network approach to multiplex digital pathology image analysis. Tasks include nuclei detection, nuclei segmentation, tumor region of interest segmentation, key marker segmentation, cellular colocalization (classification), and results integration. Our network overcomes a wide variety of qualitative challenges that are difficult if not impossible to address robustly using classical image processing methods, including variations in size, shape, intensity, hollow vs filled, and merging and overlapping nuclei. Our algorithm outperforms classical solutions in the relevant quantitative measures, achieving 94% F1 score for nuclei segmentation, and 94%-99% accuracy in cell classification, in a challenging multiplex image analysis task.
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