This repository contains poster and code for the project on the challenges of segmenting lesions in breast ultrasound images using deep learning.
Breast ultrasound imaging is broadly applied for the early detection and diagnosis of breast lesions. As a result, accurate automated detection and segmentation are essential to aid radiologists and reduce the risk of misdiagnosis. However, despite its diagnostic value, ultrasound imaging presents challenges such as speckle noise and indistinct lesion boundaries which make accurate segmentation difficult. We evaluate four state-of-the-art deep learning models on two public datasets for breast lesion segmentation.
Note: ResNet18 5 is used as encoder with ImageNet 6 pretrained weights
/code: Code using Python notebook/poster: Poster in PDF format
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Ms. Aamal Alghamdi
PhD Student, Department of Computer and Information Sciences, University of Strathclyde
Email: [email protected] -
Dr. Mohamed Elawady — Supervisor
Teaching Fellow, Department of Computer and Information Sciences, University of Strathclyde -
Dr. Andrew Abel — Co-supervisor
Lecturer B, Department of Computer and Information Sciences, University of Strathclyde
- SINAPSE annual meeting: 9th, 10th June 2025
- SICSA PhD Conference: 25th, 26th June 2025
- BMVA Summer School: 7th-11th July 2025
@misc{awady2025buschallenges,
author = {Alghamdi, Aamal and Elawady, Mohamed and Abel, Andrew},
title = {Challenges in Segmenting Lesions in Breast Ultrasound Images},
year = {2025},
howpublished = {\url{https://github.com/mawady/BUS-Challenges}},
note = {Accessed: xxxx-xx-xx}
}Footnotes
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Y. Zhang, M. Xian, H. D. Cheng, B. Shareef, J. Ding, F. Xu, K. Huang, B. Zhang, C. Ning, Y. Wang, "BUSIS: A Benchmark for Breast Ultrasound Image Segmentation," Healthcare., vol. 10, no. 4, pp. 729, Apr, 2022. ↩
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Pons, G., Mart ́ı, J., Mart ́ı, R., Ganau, S., Vilanova, J.C., Noble, J.A.: Evaluating lesion segmentation on breast sonography as related to lesion type. Journal of Ultrasound in Medicine 32(9) (2013) 1659–1670 ↩