Refereed Journal Papers

  1. 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]
  2. 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]
  3. 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]
  4. 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]

Refereed Conference Papers

  1. ​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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]​
  7. 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. [htm] [pdf] [bibTex]
  8. 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. 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]
  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. [bibTex]

Refereed Abstracts

  1. 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 RSNA, Chicago, IL, Dec 2019. Oral Presentation. (Top Conference in Radiology)
  2. 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.
  3. 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)
  4. ​​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.
  5. 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)
  6. 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 the 59th Annual Meeting and Exhibition of American Association of Physicists in Medicine (AAPM). Denver, CO, Jul 2017. Oral presentation.
  7. 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.​

Award

  • 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. 
    • ​​Received 2nd Place in the Basic Science Category
  • Gongbo Liang, Q. Li. "Pedestrian Detection Using Line Segments." In the 46th WKU Student Research Conference. Bowling Green, KY, April 2016. Oral presentation.
    • ​​Received Best Graduate Oral Paper Award in the Natural Sciences Category

Under Review

  • Gongbo Liang, C. Greenwell, Y. Zhang, X. Wang, R. Kavuluru, and N. Jacobs. "Weakly-Supervised Feature Learning for Medical Image Analysis."
  • Y. Zhang, Gongbo Liang, Y. Su, and N. Jacobs. "Parametric Attention for Sparse Image Classification."

Under Preparation 


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