Min Xian, Ph.D.

• Trustworthy artificial intelligence (AI)
• Machine learning (ML) and deep learning
• Adversarial learning
• AI/ML applications in critical areas
• Biomedical image analysis
• AI-aided materials characterization and modeling

Min Xian is an associate professor in the Department of Computer Science at University of Idaho. He received his doctorate degree in computer science from Utah State University, Logan, Utah, in 2017, and his master's in pattern recognition and intelligence systems from Harbin Institute of Technology, Harbin, China, in 2011. Xian is now the director of the Machine Intelligence and Data Analytics (MIDA) lab, a research-oriented collaborative and synergistic core to impel interdisciplinary research. Xian is an affiliate professor and doctoral supervisor of the bioinformatics and computational biology (BCB) program at University of Idaho, an affiliate of the Center for Advanced Energy Studies (CAES) and a participating faculty of the Institute for Modeling Collaboration and Innovation (IMCI). He is leading projects on AI-enhanced cancer detection (NIH) and material characterization and development (DOE). His research interests include artificial intelligence, machine learning, deep neural networks, adversarial learning, biomedical data analytics, material informatics and digital image understanding. Xian is a guest editor at Healthcare, session chair for the Association for the Advancement of Artificial Intelligence 2023 (AAAI-23) and senior program committee member for AAAI-24.

1. B. Zhang, A. Vakanski and M. Xian, "BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations," in IEEE Access, vol. 11, pp. 79480-79494, 2023, doi: 10.1109/ACCESS.2023.3298569.
2. B. Shareef, M. Xian, A. Vakanski, H. Wang, "Breast Ultrasound Tumor Classification using a Hybrid Multitask CNN-Transformer Network," in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 1-8, 2023
3. H. Wang, M. Xian, A. Vakanski, and B. Shareef, "SIAN: style-guided instance-adaptive normalization for multi-organ histopathology image synthesis," in IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1-5, 2023.
4. S. Butte, H. Wang, A. Vakanski, M. Xian, "Enhanced Sharp-GAN For Histopathology Image Synthesis," in IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1-5, 2023.
5. B. Shareef, A. Vakanski, P. E. Freer, and M. Xian, "ESTAN: Enhanced small tumor-aware network for breast ultrasound image segmentation," Healthcare, vol. 10, no. 11, pp. 2262, 2022.
6. F. Gao, Y. Ma, B. Zhang, and M. Xian, "SepNet: A neural network for directionally correlated data," Neural Networks, vol. 153, pp. 215-223, 2022/09/01/, 2022.
7. Y. Zhang, M. Xian, H.D. Cheng, B. Shareef, J. Ding, F. Xu, K. Huang, B. Zhang, C. Ning, and Y. Wang, "BUSIS: A Benchmark for Breast Ultrasound Image Segmentation," Healthcare, vol. 10, no. 4, pp. 729, 2022-04-14, 2022.
8. S. Sun, M. Xian, A Vakanski, H. Ghanem, "MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer", in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
9. J. Shi, A. Vakanski, M. Xian, J. Ding, and C. Ning, "EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer," in IEEE International Symposium on Biomedical Imaging (ISBI), 2022.
10. H. Wang, M. Xian, and A. Vakanski, "TA-Net: Topology-Aware Network for Gland Segmentation," in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 1556-1564.
11. S. Butte, H. Wang, M. Xian, and A. Vakanski, "Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis," in IEEE International Symposium on Biomedical Imaging (ISBI), 2022.
12. L. Cai, F. Xu, F. G. Di Lemma, J. J. Giglio, M. T. Benson, D. J. Murray, C. A. Adkins, J. J. Kane, M. Xian, and L. Capriotti, "Understanding fission gas bubble distribution, lanthanide transportation, and thermal conductivity degradation in neutron-irradiated α-U using machine learning," Materials Characterization, vol. 184, pp. 111657, 2022.
13. A. Vakanski, and M. Xian, "Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis," in 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021.
14. C. Shi, M. Xian, X. Zhou, H. Wang, H.D. Cheng, Multi-slice low-rank tensor decomposition based multi-atlas segmentation: Application to automatic pathological liver CT segmentation, Medical Image Analysis 73 (2021) 102152.
15. Z. Wang, C. Fan, M. Xian, Application and Evaluation of a Deep Learning Architecture to Urban Tree Canopy Mapping, Remote Sensing 13(9) (2021) 1749.
16. Y. Liao, A. Vakanski, and M. Xian, "A deep learning framework for assessing physical rehabilitation exercises," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 2, pp. 468-477, 2020.
17. A. Vakanski, M. Xian, and P. E. Freer, "Attention-enriched deep learning model for breast tumor segmentation in ultrasound images," Ultrasound in medicine & biology, vol. 46, no. 10, pp. 2819-2833, 2020.
18. M. Xian, Y. Zhang, F. Xu, B. Zhang, and J. Ding, "Automatic breast ultrasound image segmentation: A survey," Pattern Recognition, vol. 79, pp. 340-355, 2018.
19. M. Xian, and Y. Zhang, "Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains," Pattern Recognition, vol. 48, no. 2, pp. 485-497, 2015.
20. M. Xian, Y. Zhang, H.-D. Cheng, F. Xu, and J. Ding, "Neutro-connectedness cut," IEEE Transactions on Image Processing, vol. 25, no. 10, pp. 4691-4703, 2016.

1. U of I's P3-R1 grant, 2023 – 2025.
2. INL LDRD, "Artificial Intelligence Enhanced Advanced Post Irradiation Examination," Oct. 1, 2021 to Sept., 2024.
3. NRC/INL Project, Subcontract, PI: Min Xian, "Artificial intelligence/machine learning (AI/ML) applications in nuclear operating experience data analysis." 2022-2023
4. NIH COBRE, "Deep learning for breast ultrasound tumor detection," sponsor: NIH/IMCI– University of Idaho, granting agency: National Institutes of Health, duration: Feb. 1, 2019 – June 30, 2023.
5. CAES collaboration grant, "AI/ML for structure material analysis," 2023
6. U of I seed grant, "Building Capabilities in Uncertainty Quantification (UQ) for Computational Models," 2023
7. INL subcontract, "Digital twin for energy storage," 2023.
8. CAES collaboration grant, "AI/ML applications on atom probe tomography," 2022.
9. INL subcontract, "Advanced Computational Capabilities Using Artificial Intelligent and Machine Learning in Nuclear Operating Experience," 2020.
10. Microfusion Pilot Project, "Prototype Design of the AI Engine for Anomaly Detection", sponsor: Microfusion Inc., 2020.

1. U of I Idaho Falls Hardware Award, "Building GPU-based HPC capability at IF", 2023
2. Equipment and Infrastructure Support (EIS) Award, "Purchase a portable ultrasound machine to develop affordable approaches for breast cancer detection," sponsor: Office for Research and Economic Development (ORED) – University of Idaho, 2020
3. Outstanding reviewer for Pattern Recognition, 2020