  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.
