# Features Fusion for Dual-View Mammography Mass Detection

arXiv: https://arxiv.org/abs/2404.16718 · HTML: https://mlsanity.com/paper/2404.16718/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Vision Transformer with Deformable Attention](https://mlsanity.com/paper/2201.00520/) (2022)
- [Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection](https://mlsanity.com/paper/2105.10160/) (2021)
- [Cross-view Relation Networks for Mammogram Mass Detection](https://mlsanity.com/paper/1907.00528/) (2019)
- [Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection](https://mlsanity.com/paper/2209.05809/) (2022)

## Built on by (most cited first, 3 of 3)

- [Context Aware Grounded Teacher for Source Free Object Detection](https://mlsanity.com/paper/2504.15404/) (2025)
- [MGRegBench: A Novel Benchmark Dataset with Anatomical Landmarks for Mammography Image Registration](https://mlsanity.com/paper/2512.17605/) (2025)
- [Lesion-Aware Attention for Mammography Segmentation with Heterogeneous Supervision](https://mlsanity.com/paper/2610.15612/) (2026)

## Abstract (arXiv)

Detection of malignant lesions on mammography images is extremely important for early breast cancer diagnosis. In clinical practice, images are acquired from two different angles, and radiologists can fully utilize information from both views, simultaneously locating the same lesion. However, for automatic detection approaches such information fusion remains a challenge. In this paper, we propose a new model called MAMM-Net, which allows the processing of both mammography views simultaneously by sharing information not only on an object level, as seen in existing works, but also on a feature level. MAMM-Net's key component is the Fusion Layer, based on deformable attention and designed to increase detection precision while keeping high recall. Our experiments show superior performance on the public DDSM dataset compared to the previous state-of-the-art model, while introducing new helpful features such as lesion annotation on pixel-level and classification of lesions malignancy.