Deep Multi-Magnification Networks for multi-class breast cancer image segmentation Journal Article


Authors: Ho, D. J.; Yarlagadda, D. V. K.; D'Alfonso, T. M.; Hanna, M. G.; Grabenstetter, A.; Ntiamoah, P.; Brogi, E.; Tan, L. K.; Fuchs, T. J.
Article Title: Deep Multi-Magnification Networks for multi-class breast cancer image segmentation
Abstract: 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. © 2021 Elsevier Ltd
Keywords: breast cancer; medical imaging; surgery; surgical excision; diseases; tissue; image segmentation; breast carcinomas; whole slide images; computational pathology; network architecture; proposed architectures; deep multi-magnification network; multi-class image segmentation; partial annotation; implications for futures; tissue segmentation
Journal Title: Computerized Medical Imaging and Graphics
Volume: 88
ISSN: 0895-6111
Publisher: Elsevier Inc.  
Date Published: 2021-03-01
Start Page: 101866
Language: English
DOI: 10.1016/j.compmedimag.2021.101866
PUBMED: 33485058
PROVIDER: scopus
PMCID: PMC7975990
DOI/URL:
Notes: Article -- Export Date: 1 March 2021 -- Source: Scopus
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MSK Authors
  1. Lee K Tan
    144 Tan
  2. Edi Brogi
    453 Brogi
  3. Thomas   Fuchs
    26 Fuchs
  4. Matthew George Hanna
    58 Hanna
  5. David Joon Ho
    7 Ho