# Cross-view Relation Networks for Mammogram Mass Detection

arXiv: https://arxiv.org/abs/1907.00528 · HTML: https://mlsanity.com/paper/1907.00528/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Multi-view analysis of unregistered medical images using cross-view transformers](https://mlsanity.com/paper/2103.11390/) (2021)
- [Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection](https://mlsanity.com/paper/2105.10160/) (2021)
- [Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation](https://mlsanity.com/paper/2209.10478/) (2022)
- [Multi-View Hypercomplex Learning for Breast Cancer Screening](https://mlsanity.com/paper/2204.05798/) (2022)
- [Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification](https://mlsanity.com/paper/2306.10676/) (2023)
- [Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection](https://mlsanity.com/paper/2209.05809/) (2022)
- [Features Fusion for Dual-View Mammography Mass Detection](https://mlsanity.com/paper/2404.16718/) (2024)
- [M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector](https://mlsanity.com/paper/2308.06420/) (2023)
- [TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection](https://mlsanity.com/paper/2311.05192/) (2023)
- [GLAM: Geometry-Guided Local Alignment for Multi-View VLP in Mammography](https://mlsanity.com/paper/2509.10344/) (2025)

## Abstract (arXiv)

Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage. The information from the two paired views (i.e., medio-lateral oblique and cranio-caudal) are highly relational and complementary, and this is crucial for doctors' decisions in clinical practice. However, existing mass detection methods do not consider jointly learning effective features from the two relational views. To address this issue, this paper proposes a novel mammogram mass detection framework, termed Cross-View Relation Region-based Convolutional Neural Networks (CVR-RCNN). The proposed CVR-RCNN is expected to capture the latent relation information between the corresponding mass region of interests (ROIs) from the two paired views. Evaluations on a new large-scale private dataset and a public mammogram dataset show that the proposed CVR-RCNN outperforms existing state-of-the-art mass detection methods. Meanwhile, our experimental results suggest that incorporating the relation information across two views helps to train a superior detection model, which is a promising avenue for mammogram mass detection.