# Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection

arXiv: https://arxiv.org/abs/2105.10160 · HTML: https://mlsanity.com/paper/2105.10160/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 86 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Relation Networks for Object Detection](https://mlsanity.com/paper/1711.11575/) (2017)
- [RepPoints: Point Set Representation for Object Detection](https://mlsanity.com/paper/1904.11490/) (2019)
- [Volumetric and Multi-View CNNs for Object Classification on 3D Data](https://mlsanity.com/paper/1604.03265/) (2016)
- [Learning a Multi-View Stereo Machine](https://mlsanity.com/paper/1708.05375/) (2017)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [The More You Know: Using Knowledge Graphs for Image Classification](https://mlsanity.com/paper/1612.04844/) (2016)
- [3D Object Proposals using Stereo Imagery for Accurate Object Class Detection](https://mlsanity.com/paper/1608.07711/) (2016)
- [Thoracic Disease Identification and Localization with Limited Supervision](https://mlsanity.com/paper/1711.06373/) (2017)
- [Detecting and classifying lesions in mammograms with Deep Learning](https://mlsanity.com/paper/1707.08401/) (2017)
- [Iterative Visual Reasoning Beyond Convolutions](https://mlsanity.com/paper/1803.11189/) (2018)
- [Pairwise Decomposition of Image Sequences for Active Multi-View Recognition](https://mlsanity.com/paper/1605.08359/) (2016)
- [Hybrid Knowledge Routed Modules for Large-scale Object Detection](https://mlsanity.com/paper/1810.12681/) (2018)
- [Cross-view Relation Networks for Mammogram Mass Detection](https://mlsanity.com/paper/1907.00528/) (2019)
- [Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images](https://mlsanity.com/paper/1504.06692/) (2015)
- [Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images](https://mlsanity.com/paper/1805.10376/) (2018)
- [Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images](https://mlsanity.com/paper/2007.01464/) (2020)
- [Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans](https://mlsanity.com/paper/1801.08599/) (2018)

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

- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review](https://mlsanity.com/paper/2112.12596/) (2021)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis](https://mlsanity.com/paper/2309.03506/) (2023)
- [Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography](https://mlsanity.com/paper/2404.15946/) (2024)
- [Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography](https://mlsanity.com/paper/2409.18119/) (2024)
- [Delving into Ipsilateral Mammogram Assessment under Multi-View Network](https://mlsanity.com/paper/2309.02197/) (2023)
- [Multi-View Hypercomplex Learning for Breast Cancer Screening](https://mlsanity.com/paper/2204.05798/) (2022)
- [Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models](https://mlsanity.com/paper/2506.08990/) (2025)
- [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)
- [A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion](https://mlsanity.com/paper/2507.16955/) (2025)
- [Case-level Breast Cancer Prediction for Real Hospital Settings](https://mlsanity.com/paper/2310.12677/) (2023)
- [PGDiffSeg: Prior-Guided Denoising Diffusion Model with Parameter-Shared Attention for Breast Cancer Segmentation](https://mlsanity.com/paper/2410.17812/) (2024)
- [GLAM: Geometry-Guided Local Alignment for Multi-View VLP in Mammography](https://mlsanity.com/paper/2509.10344/) (2025)
- [ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic Features](https://mlsanity.com/paper/2409.15744/) (2024)
- [ManiNeg: Manifestation-guided Multimodal Pretraining for Mammography Classification](https://mlsanity.com/paper/2409.15745/) (2024)
- [Random Token Fusion for Multi-View Medical Diagnosis](https://mlsanity.com/paper/2410.15847/) (2024)
- [Multi-modal and Multi-view Fundus Image Fusion for Retinopathy Diagnosis via Multi-scale Cross-attention and Shifted Window Self-attention](https://mlsanity.com/paper/2504.09106/) (2025)
- [Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification](https://mlsanity.com/paper/2607.06309/) (2026)

## Abstract (arXiv)

Mammogram mass detection is crucial for diagnosing and preventing the breast cancers in clinical practice. The complementary effect of multi-view mammogram images provides valuable information about the breast anatomical prior structure and is of great significance in digital mammography interpretation. However, unlike radiologists who can utilize the natural reasoning ability to identify masses based on multiple mammographic views, how to endow the existing object detection models with the capability of multi-view reasoning is vital for decision-making in clinical diagnosis but remains the boundary to explore. In this paper, we propose an Anatomy-aware Graph convolutional Network (AGN), which is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability. The proposed AGN consists of three steps. Firstly, we introduce a Bipartite Graph convolutional Network (BGN) to model the intrinsic geometric and semantic relations of ipsilateral views. Secondly, considering that the visual asymmetry of bilateral views is widely adopted in clinical practice to assist the diagnosis of breast lesions, we propose an Inception Graph convolutional Network (IGN) to model the structural similarities of bilateral views. Finally, based on the constructed graphs, the multi-view information is propagated through nodes methodically, which equips the features learned from the examined view with multi-view reasoning ability. Experiments on two standard benchmarks reveal that AGN significantly exceeds the state-of-the-art performance. Visualization results show that AGN provides interpretable visual cues for clinical diagnosis.