# ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation

arXiv: https://arxiv.org/abs/2009.12894 · HTML: https://mlsanity.com/paper/2009.12894/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images](https://mlsanity.com/paper/2306.12510/) (2023)
- [Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network](https://mlsanity.com/paper/2308.02101/) (2023)
- [EUIS-Net: A Convolutional Neural Network for Efficient Ultrasound Image Segmentation](https://mlsanity.com/paper/2408.12323/) (2024)
- [Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound](https://mlsanity.com/paper/2503.20685/) (2025)
- [XBusNet: Text-Guided Breast Ultrasound Segmentation via Multimodal Vision-Language Learning](https://mlsanity.com/paper/2509.07213/) (2025)
- [Residual-SwinCA-Net: A Channel-Aware Integrated Residual CNN-Swin Transformer for Malignant Lesion Segmentation in BUSI](https://mlsanity.com/paper/2512.08243/) (2025)
- [EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer](https://mlsanity.com/paper/2201.04795/) (2022)
- [A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation](https://mlsanity.com/paper/2502.02489/) (2025)
- [A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound Semantic Segmentation](https://mlsanity.com/paper/2403.15560/) (2024)
- [BUSTR: Descriptor-Aware Vision-Language Learning for Breast Ultrasound Report Generation](https://mlsanity.com/paper/2511.20956/) (2025)
- [NULLBUS: Multimodal Mixed-Supervision for Breast Ultrasound Segmentation via Nullable Global-Local Prompts](https://mlsanity.com/paper/2512.20783/) (2025)

## Abstract (arXiv)

Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape and location are important for further tumor quantification and classification. However, segmenting small tumors in ultrasound images is challenging, due to the speckle noise, varying tumor shapes and sizes among patients, and the existence of tumor-like image regions. Recently, deep learning-based approaches have achieved great success for biomedical image analysis, but current state-of-the-art approaches achieve poor performance for segmenting small breast tumors. In this paper, we propose a novel deep neural network architecture, namely Enhanced Small Tumor-Aware Network (ESTAN), to accurately and robustly segment breast tumors. ESTAN introduces two encoders to extract and fuse image context information at different scales and utilizes row-column-wise kernels in the encoder to adapt to breast anatomy. We validate the proposed approach and compare it to nine state-of-the-art approaches on three public breast ultrasound datasets using seven quantitative metrics. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on small tumor segmentation.