# Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images

arXiv: https://arxiv.org/abs/1910.08978 · HTML: https://mlsanity.com/paper/1910.08978/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2019.

## Built on by (most cited first, 17 of 17)

- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Residual-SwinCA-Net: A Channel-Aware Integrated Residual CNN-Swin Transformer for Malignant Lesion Segmentation in BUSI](https://mlsanity.com/paper/2512.08243/) (2025)
- [Saliency-Guided Deep Learning Network for Automatic Tumor Bed Volume Delineation in Post-operative Breast Irradiation](https://mlsanity.com/paper/2105.02771/) (2021)
- [EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer](https://mlsanity.com/paper/2201.04795/) (2022)
- [InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification](https://mlsanity.com/paper/2402.02274/) (2024)
- [A Novel Approach to Breast Cancer Segmentation using U-Net Model with Attention Mechanisms and FedProx](https://mlsanity.com/paper/2510.19118/) (2025)
- [AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images](https://mlsanity.com/paper/2105.10239/) (2021)
- [Joint localization and classification of breast tumors on ultrasound images using a novel auxiliary attention-based framework](https://mlsanity.com/paper/2210.05762/) (2022)
- [Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2308.00947/) (2023)
- [BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks](https://mlsanity.com/paper/2310.00919/) (2023)
- [SPEEDNet: Salient Pyramidal Enhancement Encoder-Decoder Network for Colonoscopy Images](https://mlsanity.com/paper/2312.01128/) (2023)
- [Modifying the U-Net's Encoder-Decoder Architecture for Segmentation of Tumors in Breast Ultrasound Images](https://mlsanity.com/paper/2409.00647/) (2024)
- [Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection](https://mlsanity.com/paper/2505.17683/) (2025)
- [Externally Validated Breast Ultrasound Segmentation via Multi-task Learning with BI-RADS-Consistent Morphological Priors](https://mlsanity.com/paper/2511.15968/) (2025)

## Abstract (arXiv)

Incorporating human domain knowledge for breast tumor diagnosis is challenging, since shape, boundary, curvature, intensity, or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach for integrating visual saliency into a deep learning model for breast tumor segmentation in ultrasound images. Visual saliency refers to image maps containing regions that are more likely to attract radiologists visual attention. The proposed approach introduces attention blocks into a U-Net architecture, and learns feature representations that prioritize spatial regions with high saliency levels. The validation results demonstrate increased accuracy for tumor segmentation relative to models without salient attention layers. The approach achieved a Dice similarity coefficient of 90.5 percent on a dataset of 510 images. The salient attention model has potential to enhance accuracy and robustness in processing medical images of other organs, by providing a means to incorporate task-specific knowledge into deep learning architectures.