Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture
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Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations (≈O(0.1 μ m)) over cellular structures (≈O(10μ m)) to the global tissue architecture (O(1 mm)). Bearing in mind which information is employed by human pathologists, we introduce and examine different strategies for the integration of multiple and widely separate spatial scales into common U-Net-based architectures. Based on this, we present a family of new, end-to-end trainable, multi-scale multi-encoder fully-convolutional neural networks for human modus operandi-inspired computer vision in histopathology.
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