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.

arXiv commentAccepted at Computerized Medical Imaging and Graphics

References in corpus (7)

• A Survey on Deep Learning in Medical Image Analysis
• A systematic study of the class imbalance problem in convolutional neural networks
• Deep Learning for Identifying Metastatic Breast Cancer
• Detecting Cancer Metastases on Gigapixel Pathology Images
• Computational Pathology: Challenges and Promises for Tissue Analysis
• Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification
• Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images

Cited by in corpus (2)

• Deep neural network models for computational histopathology: A survey
• The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection