# AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms

arXiv: https://arxiv.org/abs/1810.10151 · HTML: https://mlsanity.com/paper/1810.10151/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2018.

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

- [Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining](https://mlsanity.com/paper/2402.03302/) (2024)
- [Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixing](https://mlsanity.com/paper/2501.04376/) (2025)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images](https://mlsanity.com/paper/2507.08648/) (2025)
- [Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations](https://mlsanity.com/paper/2101.07945/) (2021)
- [R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation](https://mlsanity.com/paper/2105.02290/) (2021)
- [ConnectedUNets++: Mass Segmentation from Whole Mammographic Images](https://mlsanity.com/paper/2210.13668/) (2022)
- [Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation](https://mlsanity.com/paper/2311.11176/) (2023)
- [Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations](https://mlsanity.com/paper/2403.09315/) (2024)
- [Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation](https://mlsanity.com/paper/2505.08525/) (2025)
- [SWIN-SFTNet : Spatial Feature Expansion and Aggregation using Swin Transformer For Whole Breast micro-mass segmentation](https://mlsanity.com/paper/2211.08717/) (2022)
- [AURASeg: Attention-Guided Upsampling with Residual-Assisted Boundary Refinement for Drivable-Area Segmentation](https://mlsanity.com/paper/2510.21536/) (2025)
- [Exploring Specular Reflection Inconsistency for Generalizable Face Forgery Detection](https://mlsanity.com/paper/2602.06452/) (2026)
- [BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography](https://mlsanity.com/paper/2607.10188/) (2026)
- [DualMiT-Net: Local-Global Transformer-Convolutional Fusion for Breast Mass Segmentation in Mammographic Regions of Interest](https://mlsanity.com/paper/2608.15019/) (2026)

## Abstract (arXiv)

Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting mass-centered image patches manually or automatically. However, manual patch extraction is time-consuming and automatic patch extraction brings errors that could not be compensated in the following segmentation step. In this study, we propose a novel attention-guided dense-upsampling network (AUNet) for accurate breast mass segmentation in whole mammograms directly. In AUNet, we employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block). Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high- and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three state-of-the-art fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.