# Stan: Small tumor-aware network for breast ultrasound image segmentation

arXiv: https://arxiv.org/abs/2002.01034 · HTML: https://mlsanity.com/paper/2002.01034/

## Key facts

- Cited by 9 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation](https://mlsanity.com/paper/2209.07193/) (2022)
- [ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation](https://mlsanity.com/paper/2211.02915/) (2022)
- [BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation](https://mlsanity.com/paper/2204.13342/) (2022)
- [Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network](https://mlsanity.com/paper/2308.02101/) (2023)
- [XBusNet: Text-Guided Breast Ultrasound Segmentation via Multimodal Vision-Language Learning](https://mlsanity.com/paper/2509.07213/) (2025)
- [EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer](https://mlsanity.com/paper/2201.04795/) (2022)
- [Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?](https://mlsanity.com/paper/2507.19575/) (2025)
- [BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks](https://mlsanity.com/paper/2310.00919/) (2023)
- [Externally Validated Breast Ultrasound Segmentation via Multi-task Learning with BI-RADS-Consistent Morphological Priors](https://mlsanity.com/paper/2511.15968/) (2025)

## Abstract (arXiv)

Breast tumor segmentation provides accurate tumor boundary, and serves as a key step toward further cancer quantification. Although deep learning-based approaches have been proposed and achieved promising results, existing approaches have difficulty in detecting small breast tumors. The capacity to detecting small tumors is particularly important in finding early stage cancers using computer-aided diagnosis (CAD) systems. In this paper, we propose a novel deep learning architecture called Small Tumor-Aware Network (STAN), to improve the performance of segmenting tumors with different size. The new architecture integrates both rich context information and high-resolution image features. We validate the proposed approach using seven quantitative metrics on two public breast ultrasound datasets. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors. Index