​REFEREED JOURNAL PAPERS
  1. L. Liu, Gongbo Liang, S. Xiong, and J. Wang. "A Spatiotemporal Correlation Deep Learning Network for Brain Penumbra Disease." In Neurocomputing. 2023. [doi]
  2. X. Xing, M. Rafique, H. Blanton, Gongbo Liang, Y. Zhang, C. Wang, N. Jacobs, and A. Lin. "Improvement of Alzheimer's Diseases Diagnosis with Learnable Weighted Pooling for 3D Medical Image Classification." In Electronics. 2023. In press.
  3. K. Li, F. Zheng, P Wu, Q Wang, Gongbo Liang, and L. Jiang. "Improving Pneumonia Classification and Lesion Detection Using Spatial Attention Superposition and Multi-layer Feature Fusion." In Electronics. 2022. [doi]
  4. ​Gongbo Liang, C. Greenwell, Y. Zhang, X. Wang, R. Kavuluru, and N. Jacobs. "Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging." In IEEE Journal of Biomedical and Health Informatics. 2022. [arXiv] [bibTex]
  5. S. Lin, Y. Su, Gongbo Liang, YY. Zhang, N. Jacobs, and Y. Zhang. "Estimating Cluster Masses from SDSS Multi-band Images with Transfer Learning." In Monthly Notices of the Royal Astronomical Society. 2022. [arXiv] [bibTex]
  6. L Liu, J Chang, Y Wang,  Gongbo Liang, YP Wang, and H Zhang, "Decomposition-Based Correlation Learning for Multi-Modal MRI-Based Classification of Neuropsychiatric Disorders." In Frontiers in Neuroscience. 2022.  [doi]
  7. Gongbo Liang,  L. Liu, H. Ganesh, D, Steffey, N. Jacobs, and J. Zhang. "Assessment of Enteric Feeding Tube Positioning Using Convolutional Neural Networks on Small Scale Datasets." In BMC Medical Imaging. 2022. [doi]
  8. L. Liu, J. Chang, Y. Wang, P. Zhang, Gongbo Liang, and H. Zhang. "LLRHNet: Multiple Lesions Segmentation Using Local-Long Rang Features." In Frontiers in Neuroinformatics. 2022. [doi]
  9. Y. Su, Y. Zhang, Gongbo Liang, J. A. ZuHone, D. J. Barnes, N. B. Jacobs, M. Ntampaka et al. "A deep learning view of the census of galaxy clusters in IllustrisTNG." In Monthly Notices of the Royal Astronomical Society. Oxford University Press, 2020. (impact factor: 5.356) [arXiv] [bibTex] [media] [doi]
  10. T. Hammond, X. Xing, D. Ma1, K. Nho, F. Elahi, D. Ziegler, Gongbo Liang, Q. Cheng, N. Jacobs, P. Crane, ADNI, A. Lin. "β-amyloid and tau drive early Alzheimer’s disease decline while glucose hypometabolism drives late decline."  In Communications Biology. Nature Research, 2020. [pdf] [bibTex] [doi]
  11. X. Wang, Gongbo Liang, Y. Zhang, H. Blanton, Z. Bessinger, and N. Jacobs. "Inconsistent Performance of Deep Learning Models on Mammogram Classification." In Journal of the American College of Radiology. Elsevier, 2020.  (impact factor: 4.268) [bibTex] [media] [doi]
  12. R. Mihail, Gongbo Liang, N. Jacobs. “Automatic Hand Skeletal Shape Estimation from Radiographs.” In Transactions on NanoBioscience, vol. 18, no.3, pp. 296-305. IEEE, 2019. (Impact Factor: 2.791) [html] [bibTex] [doi]
  13. Gongbo Liang, J. Zhang, M. Brooks, J. Howard, and J. Chen. "Radiomic Features of Lung Cancer and Their Dependency On Ct Image Acquisition Parameters." In Medical Physics, 44, no. 6, pp. 3024. AAPM 2017. (Impact Factor: 3.177) (Scientific Abstracts)  [html] [bibTex]
REFEREED CONFERENCE PAPERS​
  1. 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.
  2. R. Li, F. Zheng, Gongbo Liang, L. Jiang, P. Wu, and B. Chen. "ESSM: an extractive summarization model with enhanced spatial-temporal information and span mask encoding." In the 5th International Conference on Computer Information Science and Application Technology. 2022. Chongqing, China. 
  3. Y. Qu, D. Yan, E. Xing, F. Zheng, L. Liu, J. Zhang, and Gongbo Liang. "Beware the Black-Box of Medical Image Generation: An Uncertainty Analysis by the Learned Feature Space." In the 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022. Glasgow, Scotland, UK.
  4. 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.
  5. Y. Zhang, Gongbo Liang, and N. Jacobs. "Dynamic Feature Alignment for Semi-supervised Domain Adaptation." In 32nd British Machine Vision Conference (BMVC 2021). [pdf][code][webpage]
  6. L. Liu, Gongbo Liang, and J. Wang. `"Semi-Supervised Segmentation Network for Ischemic Penumbra Tissues." In 17th International Symposium on Bioinformatics Research and Applications (ISBRA), 2021. Shenzheng, China.
  7. ​Gongbo Liang, X. Xing, Y. Zhang, X. Xing, L. Liu, A. Lin, and N. Jacobs . “Alzheimer’sDisease MRI Classification Using 2D CNNs.” In Engineering in Medicine and Biology Society (EBMC), 2021 IEEE International Conference on, pp. xxx-xxx. IEEE, 2021. Forthcoming [pdf] [bibTex]
  8. Q. Yin, X. Xing, L. Liu, A. Lin, N. Jacobs, and Gongbo Liang. “Multi-Modal Data Analysis forAlzheimer’s Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features.” In Engineering in Medicine and Biology Society (EBMC), 2021. [pdf] [bibTex]
  9. Y. Zhang, Gongbo Liang, Y. Su, and N. Jacobs. "Parametric Attention for Sparse Image Classification." In Twenty-fifth International Conference on Pattern Recognition (ICPR), 2021. Milan, Italy.
  10. Y. Zhang, Gongbo Liang, Y Su, and N. Jacobs. "Multi-Branch Attention Networks for Classifying Galaxy Clusters ." In 25th International Conference on Pattern Recognition​ (ICPR2020). (Acceptance rate 28.47%) [paper] [poster] [webpage] [doi] [bibTex]
  11. ​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). (Acceptance rate 29.1%) [pdf] [poster] [project page] [bibTex]
  12. Gongbo Liang, X. Wang, Y. Zhang,and N. Jacobs. "Weakly-Supervised Self-Training Breast Cancer Localization." In Engineering in Medicine and Biology Society (EBMC), 2020 IEEE International Conference on, pp. 1124-1127. IEEE, 2020. [pdf] [bibTex]
  13. Y. Zhang, Gongbo Liang, T. Salem, and N. Jacobs. "Defense-PointNet: Protecting PointNet Against Adversarial Attacks." In BigData, 2019 IEEE International Conference on, pp. 5654-5660. IEEE, 2019. [pdf] [bibTex]
  14. Gongbo Liang, X. Wang, Y. Zhang, X. Xing, H. Blanton, T. Salem, and N. Jacobs. "Joint 2D-3D Breast Cancer Classification." In Bioinformatics and Biomedicine (BIBM), 2019 IEEE International Conference on, pp. 692-696. IEEE, 2019. [pdf][bibTex]
  15. Y. Zhang, X. Wang, H. Blanton, Gongbo Liang, X. Xing, and N. Jacobs. "2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification." In Bioinformatics and Biomedicine (BIBM), 2019 IEEE International Conference on, pp. 1013-1017. IEEE, 2019. [pdf]​ [bibTex]
  16. Gongbo Liang, J. Zhang, M. Brooks, N. Jacobs, and J. Chen. “GANai: Standardizing CT Images Using Generative Adversarial Network with Alternative Improvement.” In Healthcare Informatics (ICHI), 2019 IEEE International Conference on, pp. 1-11. IEEE, 2019. (Acceptance rate ~28%) [pdf]​ [bibTex]​
  17. Gongbo Liang, Q. Li, and X. Kang. "Pedestrian detection via a leg-driven physiology framework." In Image Processing (ICIP), 2016 IEEE International Conference on, pp. 2926-2930. IEEE, 2016. [html] [pdf] [bibTex]
  18. Q. Li, Gongbo Liang, and Y. Gong. "A geometric framework for stop sign detection." In Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on, pp. 258-262. IEEE, 2015. [pdf] [bibTex]
REFEREED WORKSHOP PAPERS​
  1. Gongbo Liang, Y. Su, S. Lin, Y. Zhang, Y. Zhang, and N. Jacobs. "Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate." In Thirty-fourth Conference on Neural Information Processing Systems​ (NeurIPS) Workshop on  Machine Learning and the Physical Sciences. NeurIPS, 2020. Vancouver, Canada. [Paper] [arXiv] [Poster] [code] [webpage] [bibTex]
  2. 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 the European Conference on Computer Vision (ECCV) Workshop on BioImage Computing. ECCV, 2020. Glasgow, United Kingdom. [html][bibTex] [code]
  3. Gongbo Liang, Y. Zhang, and N. Jacobs. "Neural Network Calibration for Medical ImagingClassification Using DCA Regularization." In International Conference on Machine Learning (ICLM) Workshop on Uncertainty & Robustness in Deep Learning. ICML, 2020. Vienna, Austria. [pdf] [bibTex]
REFEREED ABSTRACTS​
  1. Gongbo liang, J. Guerrero, and I. Alsmadi. "Mutation-Based Adversarial Attack on Neural Text Detectors." In Annual Computer Security Applications Conference. Austin, TX, Dec 2022.
  2. D. Yan, Gongbo Liang, H. Ganesh, J. Lee, and Jie Zhang. "Performance Evaluation of Advanced Deep Neural Network Methods for CT Image Denoising." In the 6th Annual Commonwealth Computational Summit. Lexington, KY, Oct 2022. Podium.
  3. 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. Award abstract.
  4. Gongbo Liang, D. Steffey, H. Ganesh, and J. Zhang. "Self-Supervised Deep Learning in the Assessment of Enteral Feeding Tube Positioning Based on A Small Dataset." In the 63rd American Association of Physicists in Medicine (AAPM) Annual Meeting. July 2021. Virtual.
  5. 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 RSNA Scientific Assembly and Annual Meeting, Chicago, IL, Dec 2019. Oral Presentation.
  6. Y. Zhang, Gongbo Liang, N. Jacobs, and X. Wang. "Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization." In C-MIMI, Austin, TX, Sep 2019. Oral Presentation.
  7. Gongbo Liang, N. Jacobs, and X. Wang. "Breast Cancer Classification Using Combined Whole Mammography and Digital Breast Tomosynthesis." In Markey Cancer Center Research Day. Lexington, KY, May 2019. Poster presentation. (Award poster)
  8. ​​Gongbo Liang, X. Wang, and N. Jacobs. “Evaluating the Publicly Available Mammography Datasets   for Deep Learning Model Training.” In 2019 SBI/ACR Breast Imaging Symposium. Hollywood, FL, April 2019. E-poster presentation.
  9. 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 Annual Symposium. San Francisco, CA, Nov 2018. Poster presentation. (Acceptance rate ~22%) (Top Conference in Medical Informatics)​
  10. Gongbo Liang, J. Zhang, M. Brooks, J. Howard, and J. Chen. “Do Lung Tumor Image Features Depend on CT Acquisition Parameters.” In American Association of Physicists in Medicine (AAPM) Ohio River Valley Spring Educational Symposium. Lexington, KY, April 2017. Oral presentation.​
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