Journals
T. Dove, Z. Xie, M. Tang, X. Ye, N. Jacobs, and Gongbo Liang. "Recent Advancements in Deep Learning for SAR-to-Optical Image Translation." IEEE Transactions on Geoscience and Remote Sensing. (UNDER REVIEW, IF=8.6, SJR=Q1, Student Work) [arXiv]
D. Deanda, I. Alsmadi, J. Guerrero, and Gongbo Liang. "Defending Mutation-Based Adversarial Text Perturbation: A Black-box Approach." Cluster Computing, 28, (2025):196. (IF=3.6, SJR=Q1, Student Work) [DOI]
L. Liu, J. Xie, J. Chang, Z. Liu, T. Sun, Gongbo Liang, and W. Gou. "H-Net: Heterogeneous neural network for multi-classification of neuropsychiatric disorders." IEEE Journal of Biomedical and Health Informatics, 28, no.9 (2024):5509 - 5518. (IF=7.7, SJR=Q1) [DOI]
Gongbo Liang, J. Zulu, X. Xing, and N. Jacobs. "Unveiling Roadway Hazards: Enhancing Fatal Crash Risk Estimation through MultiScale Aerial Images and Self-Supervised Learning." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17 (2024):535-546. (IF=5.5, SJR=Q1) [DOI] [Project Page]
L. Liu, P. Zhang, Gongbo Liang, S. Xiong, J. Wang, and G. Zhang. "A Spatiotemporal Correlation Deep Learning Network for Brain Penumbra Disease." Neurocomputing, 520, no.1 (2023):274-283. (IF=6.0, SJR=Q1) [DOI]
Gongbo Liang, C. Greenwell, Y. Zhang, X. Xing, X. Wang, R. Kavuluru, and N. Jacobs . "Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging." IEEE Journal of Biomedical and Health Informatics, 26, no. 4 (2022):1640-1649. (IF=7.7, SJR=Q1) [DOI]
S. Lin, Y. Su, Gongbo Liang, YY. Zhang, N. Jacobs, and Y. Zhang. "Estimating Cluster Masses from SDSS Multi-band Images with Transfer Learning." Monthly Notices of the Royal Astronomical Society, 512, no. 3 (2022):3885-3894. (IF=5.4, SJR=Q1) [DOI]
X. Wang, Gongbo Liang, Y. Zhang, H. Blanton, Z. Bessinger, and N. Jacobs. "Inconsistent Performance of Deep Learning Models on Mammogram Classification." Journal of the American College of Radiology, 17, no. 6 (2020): 796-803. (IF=6.3, SJR=Q1) [DOI]
Conferences
A. Elallaf, M. Chen, N. Jacobs, and Gongbo Liang. "Beta Distribution Learning for Reliable Roadway Crash Risk Assessment." Submitted to AAAI. (UNDER REVIEW, CORE2023 Ranking:A*, H5-Index: 220, Student Work)
B. Han, Y. Masupalli, X. Xing, and Gongbo Liang. "Improving Medical Imaging Model Calibration through Probabilistic Embedding" In IEEE International Conference on Big Data (BigData), 2024. Washington DC, USA. (H5-index: 54, Acceptance Rate: ~18%, Student Work) [DOI]
B. Han, C. Moran, J. Yang, Y. Lee, Z. Cao, and Gongbo Liang. "Multi-Scale Self-Supervised Consistency Training for Trustworthy Medical Imaging Classification." In the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2024. Orlando, FL. Oral Presentation (Acceptance Rate: ~18% [690/3850], Student Work) [DOI]
E. Xing, L. Liu, X. Xing, Y Qu, N. Jacobs, and Gongbo Liang. "Neural Network Decision-Making Criteria Consistency Analysis via Inputs Sensitivity." In the 26th International Conference on Pattern Recognition (ICPR), 2022. Montréal, Québec, Canada. (CORE Raking: B, H5-Index: 68, Student Work) [DOI]
X. Xing, Gongbo Liang, Y. Zhang, S. Khanal, A. Lin, and N. Jacobs. "ADViT: Vision Transformer on Multi-modality PET Images for Alzheimer Disease Diagnosis." In IEEE International Symposium on Biomedical Imaging (ISBI), 2022. Kolkata, India. (ERA Raking: A, H5-Index: 59) [DOI]
Y. Zhang, Gongbo Liang, and N. Jacobs. "Dynamic Feature Alignment for Semi-Supervised Domain Adaptation." In 32nd British Machine Vision Conference (BMVC), 2021. Manchester, England. (CORE Ranking: A, H5-Index: 77, Acceptance Ratio: ~26%) [DOI]
Gongbo Liang, Y. Zhang, X. Wang, and N. Jacobs . "Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification." In 31st British Machine Vision Conference (BMVC), 2020. Manchester, England. (CORE Ranking: A, H5-Index: 77, Acceptance Rate: ~21%) [arXiv]
Y. Zhang, Gongbo Liang, T. Salem, and N. Jacobs. "Defense-PointNet: Protecting PointNet Against Adversarial Attacks." In IEEE International Conference on Big Data (Big Data), 2019. Los Angeles, USA. (H5-Index: 53, Acceptance Ratio: ~19%) [DOI]
Gongbo Liang, Q. Li, and X. Kang. "Pedestrian detection via a leg-driven physiology framework." In IEEE International Conference on Image Processing (ICIP), 2016. Phoenix, USA. (H5-Index: 61) [DOI]
Workshop Papers
D. Deanda, Y. Masupalli, J. Yang, Y. Lee, Z. Cao and Gongbo Liang. "Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval." In the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI) Workshop, 2025. Philadelphia, PA. USA. Student Work. [arXiv]
R. Jonnala, Gongbo Liang, J. Yang, and I. Alsmadi. "Potential and Challenges of Large Language Models in Public Transportation: San Antonio Case Study." In the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI) Workshop, 2025. Philadelphia, PA, USA. Student Work. [arXiv]
Gongbo Liang, S. Lin, Y. Zhang, Y. Su, and N. Jacobs . "Optical Wavelength Guided Feature Learning for Galaxy Group Richness Estimation." In 34th Conference on Neural Information Processing Systems (NeurIPS) Workshop, 2020. Vancouver, Canada. [paper]
X. Xing, Gongbo Liang, H. Blanton, M. Rafique, C. Wang, A. Lin, and N. Jacobs. "Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification." In 2020 the European Conference on Computer Vision (ECCV) Workshop, 2020. Glasgow, United Kingdom. (Joint First Author) [DOI]
Abstracts
G. Crumrine, Y.P. Masupalli, and Gongbo Liang "Probabilistic Embedding for Medical Imaging Model Calibration via Gaussian Distribution." In IEEE EMBS Lone Star Section Workshop on AI and Healthcare. San Marcos, TX, Dec, 2024. (Best Abstract)
E. Xing and Gongbo Liang. "Can We Trust Neural Networks? An Analysis of Neural Network Uncertainty by the Learned Feature Space." In ACM Mid-Southeast (ACM-MidSE) Conference. Gatlinburg, TN, Nov 2021. Podium. (Third Place Best Abstract)
Gongbo Liang, N. Jacobs, and X. Wang. "Training Deep Learning Models as Radiologists: Breast Cancer Classification Using Combined Whole 2D Mammography and Full Volume Digital Breast Tomosynthesis." In the 105th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA). Chicago, IL, Dec 2019. Podium. (A* Radiology Conference, Acceptance Rate: 1664/13419=~12%)
Gongbo Liang, J. Zhang, M. Brooks, J. Howard, and J. Chen. "Enhancing Radiomic Features of CT Images using Generative Adversarial Network with Alternative Improvement." In AMIA 2018 Annual Symposium. San Francisco, CA, Nov 2018. (A* Medical Informatics Conference)
