Meng Xu

Meng Xu

Assistant Professor | Department of Computer Science and Technology | Kean University

Trustworthy Weakly Supervised Breast Cancer Detection in Ultrasound Imaging

Funding

Students Partnering with Faculty (SpF) program at Kean University

Period

Summer 2024

Role

Principal Investigator

Overview

This project investigated trustworthy weakly supervised breast cancer detection in breast ultrasound imaging. It focused on BUSwiNet, a weakly supervised framework using image-level labels to identify breast tumor bounding boxes and classify tumors. The project also examined Bayesian neural networks for quantitative trustworthiness evaluation and supported undergraduate research mentoring in AI and healthcare.

Team

  • Principal Investigator: Dr. Meng Xu
  • Co-Principal Investigator: Dr. Kuan Huang
  • Undergraduate Student Researchers:
    • Maryam Ahmed
    • Joanna Loja
    • Armando Mendez

Research Outcomes

  • Maryam Ahmed, Joanna Loja, Kuan Huang, Meng Xu. “Benchmarking the Robustness of Segmentation Methods Against Adversarial Attacks in Breast Ultrasound Segmentation.” International Conference on Computational Science and Computational Intelligence, 2024. [link]
  • Armando Mendez, Meng Xu, Kuan Huang. “Multimodal Breast Ultrasound Segmentation: Combining Visual and Clinical Data.” International Conference on Computational Science and Computational Intelligence, 2024. [link]
  • Cesar Marte, Meng Xu, Kuan Huang. “Text-Guided Weakly Supervised Segmentation for COVID-19 Detection in X-ray Images.” International Conference on Computational Science and Computational Intelligence, 2024. [link]

Acknowledgment

This project was funded by the Students Partnering with Faculty (SpF) program at Kean University.