Pathologic analysis of surgical excision specimens for breast carcinoma is
important to evaluate the completeness of surgical excision and has
implications for future treatment. This analysis is performed manually by
pathologists reviewing histologic slides prepared from formalin-fixed tissue.
In this paper, we present Deep Multi-Magnification Network trained by partial
annotation for automated multi-class tissue segmentation by a set of patches
from multiple magnifications in digitized whole slide images. Our proposed
architecture with multi-encoder, multi-decoder, and multi-concatenation
outperforms other single and multi-magnification-based architectures by
achieving the highest mean intersection-over-union, and can be used to
facilitate pathologists' assessments of breast cancer.

本文介绍了深度多放大率网络，采用多级别数字化全层切片图像的一组修补程序对部分注释进行训练，实现对乳腺癌多类组织的自动化分割，表现出高交叉共同区的意义，可用于协助病理学家评估乳腺癌。