  Automated digital histopathology image segmentation is an important task to
help pathologists diagnose tumors and cancer subtypes. For pathological
diagnosis of cancer subtypes, pathologists usually change the magnification of
whole-slide images (WSI) viewers. A key assumption is that the importance of
the magnifications depends on the characteristics of the input image, such as
cancer subtypes. In this paper, we propose a novel semantic segmentation
method, called Adaptive-Weighting-Multi-Field-of-View-CNN (AWMF-CNN), that can
adaptively use image features from images with different magnifications to
segment multiple cancer subtype regions in the input image. The proposed method
aggregates several expert CNNs for images of different magnifications by
adaptively changing the weight of each expert depending on the input image. It
leverages information in the images with different magnifications that might be
useful for identifying the subtypes. It outperformed other state-of-the-art
methods in experiments.
