This project focuses on developing computational tools for analyzing X-ray mammography and ultrasound imaging data related to breast cancer and lung diseases.
Citation
- Kuan Huang, Meng Xu, and Yingfeng Wang*, “Using Adversarial Training to Improve Uncertainty Quantification,” IEEE Transactions on Artificial Intelligence, vol. 7, no. 1, pp. 522-533, 2026.
- Kuan Huang*, Noorul Sahel, Dikshya Karki, Meng Xu, and Yingfeng Wang*, “One Pixel Can Change the Diagnosis: Adversarial and Non-Adversarial Robustness and Uncertainty in Breast Ultrasound Classification Model,” Proceedings of the AAAI Symposium Series, vol. 7, no. 1, pp. 524-529, 2025.
- Meng Xu, Yingfeng Wang, and Kuan Huang*, “AnatoSegNet: Anatomy Based CNN-Transformer Network for Enhanced Breast Ultrasound Image Segmentation,” Proceedings of the 22nd IEEE International Symposium on Biomedical Imaging (ISBI), 2025.
- Kuan Huang*, Yingfeng Wang, and Meng Xu, “Investigating the Fairness of Deep Learning Models in Breast Cancer Diagnosis Based on Race and Ethnicity,” Proceedings of the AAAI Symposium Series, vol. 4, no. 1, pp. 303-307, 2024.