# Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation

arXiv: https://arxiv.org/abs/1910.13042 · HTML: https://mlsanity.com/paper/1910.13042/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 22 of 22)

- [Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images](https://mlsanity.com/paper/2204.12454/) (2022)
- [OCELOT: Overlapped Cell on Tissue Dataset for Histopathology](https://mlsanity.com/paper/2303.13110/) (2023)
- [Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs](https://mlsanity.com/paper/2103.03129/) (2021)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer](https://mlsanity.com/paper/2301.02933/) (2023)
- [Efficient High-Resolution Deep Learning: A Survey](https://mlsanity.com/paper/2207.13050/) (2022)
- [H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma](https://mlsanity.com/paper/2206.10573/) (2022)
- [DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detection](https://mlsanity.com/paper/2303.02602/) (2023)
- [Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation](https://mlsanity.com/paper/2203.15015/) (2022)
- [A QuadTree Image Representation for Computational Pathology](https://mlsanity.com/paper/2108.10873/) (2021)
- [Stain-invariant self supervised learning for histopathology image analysis](https://mlsanity.com/paper/2211.07590/) (2022)
- [A review of machine learning approaches, challenges and prospects for computational tumor pathology](https://mlsanity.com/paper/2206.01728/) (2022)
- [Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation](https://mlsanity.com/paper/2205.01777/) (2022)
- [Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning](https://mlsanity.com/paper/2307.04472/) (2023)
- [Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction](https://mlsanity.com/paper/2208.04910/) (2022)
- [Segmentation, Classification and Interpretation of Breast Cancer Medical Images using Human-in-the-Loop Machine Learning](https://mlsanity.com/paper/2403.20112/) (2024)
- [Robust Tumor Detection from Coarse Annotations via Multi-Magnification Ensembles](https://mlsanity.com/paper/2303.16533/) (2023)
- [The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection](https://mlsanity.com/paper/2306.16989/) (2023)
- [Multi-Stain Multi-Level Convolutional Network for Multi-Tissue Breast Cancer Image Segmentation](https://mlsanity.com/paper/2406.05828/) (2024)
- [Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion](https://mlsanity.com/paper/2406.14287/) (2024)
- [GNCAF: A GNN-based Neighboring Context Aggregation Framework for Tertiary Lymphoid Structures Semantic Segmentation in WSI](https://mlsanity.com/paper/2505.08430/) (2025)
- [Adapting Vision Transformers to Ultra-High Resolution Semantic Segmentation with Relay Tokens](https://mlsanity.com/paper/2601.05927/) (2026)

## Abstract (arXiv)

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.