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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