Recent Publications
2020 - 2022:- 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.
- J. Shi, A. Vakanski, M. Xian, J. Ding and C. Ning, "EMT-NET:
Efficient Multitask Network for Computer-Aided Diagnosis of Breast
Cancer," 2022 IEEE 19th International Symposium on Biomedical Imaging
(ISBI), 2022, pp. 1-5, doi: 10.1109/ISBI52829.2022.9761438.
- S. Butte, H. Wang, M. Xian, and A. Vakanski, “Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis,” in IEEE ISBI, 2022.
- H. Wang, M. Xian, and A. Vakanski, “TA-Net: Topology-Aware Network for Gland Segmentation,” In IEEE WACV, 2022.
- 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, L. Capriotti, and T. Yao,
“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, 2022/02/01/, 2022.
- B. Zhang, A. Vakanski, and M. Xian, “BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images,” in 31st IEEE MLSP, 2021.
- A. Vakanski, and M. Xian, “Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis,” in 31st IEEE MLSP, 2021.
- C. Shi, M. Xian, X. Zhou, H. Wang, and H.-D. Cheng, “Multi-slice
low-rank tensor decomposition based multi-atlas segmentation:
Application to automatic pathological liver CT segmentation,” Medical Image Analysis, vol. 73, pp. 102152, 2021-10-01, 2021. IF = 8.55
- H. Wang, A. Vakanski, C. Shi, and M. Xian, “Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images,” arXiv preprint arXiv:2109.15283, 2021.
- Z. Wang, C. Fan, and M. Xian, “Application and Evaluation of a Deep Learning Architecture to Urban Tree Canopy Mapping,” Remote Sensing, vol. 13, no. 9, pp. 1749, 2021.
- H. Wang, M. Xian, and A. Vakanski,
“Bending Loss Regularized Network for Nuclei Segmentation in
Histopathology Images,” in IEEE International Symposium on Biomedical
Imaging (ISBI), Iowa City, IA, USA, 2020, pp. 1-5.
- F. Xu, Y. Zhang, M. Xian, H.D. Cheng, B. Zhang, J. Ding, C. Ning, and Y. Wang, “Breast Anatomy Enriched Tumor Saliency Estimation,” in 25th International Conference on Pattern Recognition, 2020.
- A. Vakanski, M. Xian, and P. Freer,
“Attention Enriched Deep Learning Model for Breast Tumor Segmentation
in Ultrasound Images,” Ultrasound in Medicine & Biology, 2020. IF=2.51
- B. Shareef, M. Xian, and A. Vakanski, “STAN: Small tumor-aware network for breast ultrasound image segmentation,” in IEEE 17th International Symposium on Biomedical Imaging, 2020.
- Y. Liao, A. Vakanski, M. Xian, D. Paul,
and R. Baker, “A review of computational approaches for evaluation of
rehabilitation exercises,” Computers in Biology and Medicine, vol. 119, pp. 103687, 2020/04/01/, 2020. IF=3.43
- Y. Liao, A. Vakanski, and M. Xian, “A Deep Learning Framework for Assessing Physical Rehabilitation Exercises,” IEEE Trans Neural Syst Rehabil Eng, vol. 28, no. 2, pp. 468-477, Feb, 2020. IF=4.11
- H. Guan, Y. Zhang, M. Xian, H. Cheng,
and X. Tang, “SMOTE-WENN: Solving class imbalance and small sample
problems by oversampling and distance scaling,” Applied Intelligence, pp. 1-16, 2020. IF=3.26
- B. Hersh, A. Mohajeri, A. Mirkouei, and
M. Xian, “Cyber-Physical Infrastructures for Advancing Pyrolysis
Conversion Process: A Case Study of Biochar Production,” in
International Design Engineering Technical Conferences and Computers
and Information in Engineering Conference, 2020, pp. V009T09A002.
- M. B. Badsha, R. Li, B. Liu, Y. I. Li, M. Xian, N. E. Banovich, and A. Fu, “Imputation of single-cell gene expression with an autoencoder neural network,” Quantitative Biology, vol. 8, no. 1, pp. 78-94, 2020.
- H. Guan, Y. Zhang, H.D. Cheng, M. Xian, and X. Tang, “BA2Cs:
Bounded abstaining with two constraints of reject rates in binary
classification,” Neurocomputing, vol. 357, pp. 125-134, 2019. IF=5.19
- F. Xu, Y. Zhang, M. Xian, H. Cheng, B. Zhang, J. Ding, C. Ning, and Y. Wang, “Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling,” arXiv preprint arXiv:1906.07760, 2019.
- S. Hansen, A. Mirkouei, and M. Xian, “Cyber-Physical Control and Optimization for Biofuel 4.0,” in IISE Annual Conference & Expo, New Orleans, 2019.
- A. Acharya, Y. Hou, Y. Mao, M. Xian, and J. Yuan, “Workload-Aware Task Placement in Edge-Assisted Human Re-identification,” in 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2019, pp. 1-9.
- M. Xian, Y. Zhang, H. D. Cheng, F. Xu, J. Ding, Automatic
breast ultrasound image segmentation: a survey, Pattern Recognition,
vol. 79, pp. 340-355, 2018. IF=7.20
- F. Xu, M. Xian, Y. Zhang, K. Huang,
H.-D. Cheng, B. Zhang, J. Ding, C. Ning, and Y. Wang, “A Hybrid
Framework for Tumor Saliency Estimation,” in International Conference
on Pattern Recognition (ICPR), Beijing, China, 2018, pp. 3935-3940.
- B. Zhang, Y. Zhang, H. Cheng, M. Xian, S. Gai, O. Cheng, and K. Huang, Computer-Aided Knee Joint Magnetic Resonance Image Segmentation-A Survey, arXiv preprint arXiv:1802.04894, 2018.
- M. Xian, Y. Zhang, H. Cheng, F. Xu, K. Huang, B. Zhang, J. Ding, C. Ning, and Y. Wang, “A Benchmark for Breast Ultrasound Image Segmentation (BUSIS),” arXiv preprint arXiv:1801.03182, 2018.
- F. Wei, J. Ding, C. Ning, F. Xu, M. Xian, and Y. Zhang, “Texture analysis and imbalanced data processing for papillary thyroid microcarcinoma detection,” in International Conference on Biological Information and Biomedical Engineering, Shanghai, China, 2018, pp. 1-4.
- Y. Luo, M. Xian, M. Mohanpurkar,
B. P. Bhattarai, A. Medarn, R. Kadavil, and R. Hovsapian, “Optimal
Scheduling of Electrolyzer in Power Market with Dynamic Prices,” in
IEEE International Conference on Probabilistic Methods Applied to Power
Systems (PMAPS), Boise, ID, USA, 2018, pp. 1-6.
- Y. Wang, R. Ni, Y. Zhao, and M.
Xian, “Watermark Embedding for Direct Binary Searched Halftone Images
by Adopting Visual Cryptography,” Computers, Materials & Continua (CMC), 2018.
- M. Xian, Y. Zhang, H. D. Cheng, F. Xu, J. Ding, Neutro-Connectedness Cut, IEEE Transactions on Image Processing, 25(2016) 4691-4703. [arXiv | Datasets: Grabcut, NC-Cut, Looseness28] IF=8.98
- H. Guan, Y. Zhang, M. Xian, H.D. Cheng, X. Tang, WENN for Individualized Cleaning in Imbalanced Data, in ICPR, 2016, pp. 456-461.
- M. Xian, F. Xu, H. D. Cheng, EISeg: Effective Interactive Segmentation, in ICPR , 2016, pp. 1982-1987. [PDF, Code avaliable upon request]
- F. Xu, M. Xian, H. D. Cheng, Unsupervised Saliency Estimation based on Robust Hypotheses, in IEEE WACV, 2016, pp. 1-6. [PDF, Code avaliable upon request]
- C. Liu, H. D. Cheng, Y. Wang, Y. Zhang, M. Xian, Robust Multiple Cue Fusion Based High-Speed and Non-Rigid Object Tracking Algorithm for Short Track Speed Skating, Journal of Electronic Imaging, 25 (2016), 0130141-01301416. [Link]
- M. Xian, Y. Zhang, H. D. Cheng, Fully Automatic Segmentation of Breast Ultrasound Images Based on Breast Characteristics in Space and Frequency Domains, Pattern Recognition, 48(2015) 485-497. [PDF, Poster, Demo: Seed generation, Segmentation] IF=7.20
- J. Ding, H. D. Cheng, M. Xian, Y.
Zhang, F. Xu,
Local-weighted Citation-kNN algorithm for breast ultrasound image
classification, Optik
- International Journal for Light and Electron Optics, 126
(2015) 5188-5193. [Link] IF=2.11
- J. Ding, M. Xian,
H. D. Cheng, An algorithm based on LBPV and MIL for left atrial thrombi
detection using transesophageal echocardiography, in IEEE ICIP, 2015,
pp. 4224-4227. [PDF]
- H. Shao, Y. Zhang, M. Xian, H. D. Cheng, F. Xu, J. Ding, A Saliency model for automated tumor detection in breast ultrasound images, in IEEE ICIP, 2015, pp. 1424-1428. [PDF]
- M. Xian, Y. Zhang, H. D. Cheng, A Fully Automatic Breast Ultrasound Image Segmentation Approach Based on Neutro-Connectedness, in ICPR, 2014, pp. 2495-2500. [PDF]
- H. Yu, M. Xian, X. Qi, Unsupervised Co-segmentation Based on Co-saliency Model and a New Global GMM Constraint in MRF, in IEEE ICIP, 2014, pp. 4412-4416. [PDF]