# A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms

arXiv: https://arxiv.org/abs/2112.04490 · HTML: https://mlsanity.com/paper/2112.04490/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks](https://mlsanity.com/paper/1703.07047/) (2017)

## Built on by (most cited first, 15 of 15)

- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [MAM-E: Mammographic synthetic image generation with diffusion models](https://mlsanity.com/paper/2311.09822/) (2023)
- [Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis](https://mlsanity.com/paper/2309.03506/) (2023)
- [Delving into Ipsilateral Mammogram Assessment under Multi-View Network](https://mlsanity.com/paper/2309.02197/) (2023)
- [An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph](https://mlsanity.com/paper/2208.03545/) (2022)
- [Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese](https://mlsanity.com/paper/2209.04794/) (2022)
- [In-context Cross-Density Adaptation on Noisy Mammogram Abnormalities Detection](https://mlsanity.com/paper/2306.06893/) (2023)
- [A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms](https://mlsanity.com/paper/2203.10609/) (2022)
- [Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices](https://mlsanity.com/paper/2208.03403/) (2022)
- [Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification](https://mlsanity.com/paper/2401.03912/) (2024)
- [MamT4: Multi-view Attention Networks for Mammography Cancer Classification](https://mlsanity.com/paper/2411.01669/) (2024)
- [Random Token Fusion for Multi-View Medical Diagnosis](https://mlsanity.com/paper/2410.15847/) (2024)
- [GIIM: Graph-based Learning of Inter- and Intra-view Dependencies for Multi-view Medical Image Diagnosis](https://mlsanity.com/paper/2603.09446/) (2026)
- [MAM-CLIP: Vision-Language Pretraining on Mammography Atlases for BI-RADS Classification](https://mlsanity.com/paper/2605.19359/) (2026)
- [Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis](https://mlsanity.com/paper/2607.26043/) (2026)

## Abstract (arXiv)

Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that the combination of multi-view analysis improved the overall breast exam classification. In this paper, we propose a novel multi-view DL approach for BI-RADS and density assessment of mammograms. The proposed approach first deploys deep convolutional networks for feature extraction on each view separately. The extracted features are then stacked and fed into a Light Gradient Boosting Machine (LightGBM) classifier to predict BI-RADS and density scores. We conduct extensive experiments on both the internal mammography dataset and the public dataset Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed approach outperforms the single-view classification approach on two benchmark datasets by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM dataset). These results highlight the vital role of combining multi-view information to improve the performance of breast cancer risk prediction.