ArticleHealthcare (Basel, Switzerland)2022
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 59 citations in OpenAlex.
- Synthetic-to-Clinical Ensemble Learning for Volumetric Breast Tumor Segmentation in Digital Breast Tomosynthesis Under Limited Annotated Data.Diagnostics (Basel, Switzerland) · 2026Article
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- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- Breast Ultrasound Image Segmentation Integrating Mamba-CNN and Feature Interaction.Sensors (Basel, Switzerland) · 2025Article
- XBusNet: Text-Guided Breast Ultrasound Segmentation via Multimodal Vision-Language Learning.Diagnostics (Basel, Switzerland) · 2025Article
- A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation.Journal of imaging informatics in medicine · 2025Article
- HMA-Net: a hybrid mixer framework with multihead attention for breast ultrasound image segmentation.Frontiers in artificial intelligence · 2025Article
- Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.Scientific reports · 2024Article
- DMAeEDNet: Dense Multiplicative Attention Enhanced Encoder Decoder Network for Ultrasound-Based Automated Breast Lesion Segmentation.IEEE access : practical innovations, open solutions · 2024Article
- Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
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- Fully automatic tumor segmentation of breast ultrasound images with deep learning.Journal of applied clinical medical physics · 2023Article
- EMT-NET: EFFICIENT MULTITASK NETWORK FOR COMPUTER-AIDED DIAGNOSIS OF BREAST CANCER.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
- Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
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 in 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 the Enhanced Small Tumor-Aware Network (ESTAN), to accurately and robustly segment breast tumors. The Enhanced Small Tumor-Aware Network introduces two encoders to extract and fuse image context information at different scales, and utilizes row-column-wise kernels to adapt to the breast anatomy. We compare ESTAN and nine state-of-the-art approaches using seven quantitative metrics on three public breast ultrasound datasets, i.e., BUSIS, Dataset B, and BUSI. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on small tumor segmentation. Specifically, the Dice similarity coefficient (DSC) of ESTAN on the three datasets is 0.92, 0.82, and 0.78, respectively; and the DSC of ESTAN on the three datasets of small tumors is 0.89, 0.80, and 0.81, respectively.
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