Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture

Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations ($\approx \mathcal{O}(0.1 \mu m)$) over cellular structures ($\approx \mathcal{O}(10\mu m)$) to the global tissue architecture ($\gtrapprox \mathcal{O}(1 mm)$). 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.