Abdullah-Al-Zubaer Imran

Abdullah-Al-Zubaer Imran

Assistant Professor of Computer Science, University of Kentucky

Publications

You can also find my publications from my Google Scholar profile.

Refereed Journal Publications

  • W. Lee, F. Wagner, A. Galdran, Y. Shi, W. Xia, G. Wang, X. Mou, M. Ahamed, A. Imran, J. Oh, K. Kim, J. Baek, D. Lee, B. Hong, P. Tempelman, D. Lyu, A. Kuiper, L. van Blokland, M. Calisto, S. Hsieh, M. Han, J. Baek, A. Maier, A. Wang, G. Gold, J. Choi, “Low-dose computed tomography perceptual image quality assessment,” Medical Image Analysis (MedIA), 2025. (paper, code)
  • A. Haque, A. Wang, A. Imran, “Task-specific self-supervision for CT image denoising,” Computer Methods in Biomechanics and Biomedical Engineering (CMBBE): Imaging & Visualization, 2023. (paper, project, code)
  • A. Haque, A. Imran, A. Wang, D. Terzopoulos, “Generalized multi-task learning from substantially unlabeled multisource medical image data,” Machine Learning for Biomedical Imaging (MELBA), 2021. (paper, project, code)
  • C. Sandino, E. Cole, C. Alkan, A. Chaudhari, A. Loening, D. Hyun, J. Dahl, A. Imran, A. Wang, S. Vasanawala, “Upstream machine learning in radiology,” Radiologic Clinics of North America (RCNA): Artificial Intelligence in Radiology, 2021. (paper)
  • A. Imran, A. Hatamizadeh, S. Ananth, X. Ding, N. Tajbakhsh, D. Terzopoulos, “Fast and automatic segmentation of pulmonary lobes from chest CT using a progressive dense V-network,” Computer Methods in Biomechanics and Biomedical Engineering (CMBBE): Imaging and Visualization, 2020. (paper) [Best Paper Award of the 2019-2020 biennium, Cover Article]

Peer-Reviewed Conference Publications

  • K. Rifa, M. Ahamed, J. Zhang, A. Imran, “Task-focused knowledge transfer from natural images for CT image quality assessment,” SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, 2025.
  • C. Archbold, U. Hassan, N. Sakib, S. Cheung, A. Imran, “Privacy preserving protest dynamics,” 16th IEEE International Workshop on Information Forensics and Security (WIFS), 2024.
  • N. Munia, A. Imran, “DermDiff: Generative diffusion model for mitigating racial biases in dermatology diagnosis,” MICCAI Workshop on Advancing Data Solutions in Medical Imaging AI (ADSMI), Marrakesh, Morocco, 2024.
  • A. Moseley, A. Imran, “PolyCL: Context-aware contrastive learning for image segmentation,” IEEE International Symposium on Biomedical Imaging (ISBI), 2024. (paper)
  • M. Ahamed, B. McFarland, X. Wang, J. Chen, A. Imran, “Automatic detection of breast cancer lumpectomy margin from intraoperative specimen mammography,” International Workshop on Breast Imaging, Chicago, IL, USA, 2024. (paper)
  • M. Medrano, S. Wang, A. Imran, G. Stevens, J.R. Tse, A. Wang, “Personalized, scout-based estimation of prospective optimization of CT tube current modulation,” SPIE Medical Imaging: Physics of Medical Imaging, San Diego, CA, USA, 2024. (paper)
  • S. Wang, M. Medrano, A. Imran, G. Stevens, J.R. Tse, A. Wang, “Retrospective tube current modulation optimization of individualized organ-level CT dose and image quality,” SPIE Medical Imaging: Physics of Medical Imaging, San Diego, CA, 2024. (paper)
  • Y. Jiang, S. Gupta, A. Imran, “Transforming radiology workflows: Pretraining for automated chest X-ray report generation,” Medical Imaging with Deep Learning (MIDL), Nashville, TN, USA, 2023. (paper)
  • M. Ahamed, J. Chen, A. Imran, “FFCL: Forward-Forward contrastive learning for improved medical image classification,” Medical Imaging with Deep Learning (MIDL), Nashville, TN, USA, 2023. (paper)
  • A. Imran, S. Wang, D. Pal, S. Dutta, E. Zucker, A. Wang, “Multimodal contrastive learning for prospective personalized estimation of CT organ dose,” Medical Image Computing and Computer Assisted Intervention (MICCAI), Singapore, 2022. (paper) [Early accept, Top 13%]
  • M. Ahamed, A. Imran, “Joint learning with local and global consistency for improved medical image segmentation,” Medical Image Understanding and Analysis (MIUA), Cambridge, UK, 2022. (paper)
  • A. Imran, D. Pal, S. Wang, S. Dutta, B. Patel, E. Zucker, A. Wang, “Personalized CT organ noise estimation from scout images,” SPIE Medical Imaging: Physics of Medical Imaging, San Diego, CA, USA, 2022. (paper)
  • A. Haque, A. Wang, A. Imran, “Noise2Quality: Non-reference, pixel-wise assessment of low dose CT image quality,” SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, San Diego, CA, USA, 2022. (paper)
  • H. Jahan, A. Imran, “LightSeg: Efficient yet effective medical image segmentation,” IEEE International Symposium on Biomedical Imaging (ISBI), Kolkata, India, 2022. (paper)
  • M. Shabanian, A. Imran, A. Siddiqui, R. Davis, J. Bissler, “3D deep neural network to automatically identify TSC structural brain pathology based on MRI,” SPIE Medical Imaging: Image Processing, San Diego, CA, USA, 2022. (paper)
  • A. Purpura-Pontoniere, A. Imran, T. Bhattacharya, “Efficient ATR using contrastive learning,” SPIE Defense: Automatic Target Recognition, Orlando, FL, USA, 2022. (paper)
  • A. Imran, S. Wang, D. Pal, S. Dutta, B. Patel, E. Zucker, A. Wang, “Personalized CT organ dose estimation from scout images,” Medical Image Computing and Computer Assisted Intervention (MICCAI), Strasbourg, France, 2021. (paper) [Early accept, Top 13%]
  • A. Haque, A. Wang, A. Imran, “Window-level is a strong denoising surrogate,” Medical Image Computing and Computer Assisted Intervention (MICCAI): Machine Learning in Medical Imaging (MLMI), Strasbourg, France, 2021. (paper, project, code)
  • A. Imran, D. Pal, B. Patel, A. Wang, “SSIQA: Multi-task learning for non-reference CT image quality assessment with self-supervised noise level prediction,” IEEE International Symposium on Biomedical Imaging (ISBI), Nice, France, 2021. (paper)
  • A. Haque, A. Imran, A. Wang, D. Terzopoulos, “MutliMix: Sparingly supervised extreme multitask learning from medical images,” IEEE International Symposium on Biomedical Imaging (ISBI), Nice, France, 2021. (paper, project, code)
  • A. Imran, D. Terzopoulos, “Progressive adversarial semantic segmentation,” International Conference on Pattern Recognition (ICPR 2020), Milan, Italy, 2021. (paper)
  • A. Imran, C. Huang, H. Tang, W. Fan, Y. Xiao, D. Hao, Z. Qian, D. Terzopoulos, “Partly supervised multitask learning,” International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 2020. (paper)
  • A. Imran, C. Huang, H. Tang, W. Fan, K. Cheung, M. To, Z. Qian, D. Terzopoulos, “Fully-automated analysis of scoliosis from spinal X-ray images,” Computer Based Medical Systems (CBMS), Rochester, MN, USA, 2020. (paper)
  • A. Imran, D. Terzopoulos, “Multi-adversarial variational autoencoder networks,” International Conference on Machine Learning and Applications (ICMLA), Boca Raton, FL, USA, 2019. (paper)
  • A. Imran, D. Terzopoulos, “Semi-supervised multi-task learning with chest X-ray images,” Medical Image Computing and Computer Assisted Intervention (MICCAI): Machine Learning in Medical Imaging (MLMI), Shenzhen, China, 2019. (paper)
  • A. Imran, A. Hatamizadeh, S. Ananth, X. Ding, D. Terzopoulos, N. Tajbakhsh, “Automatic segmentation of pulmonary lobes using a progressive dense V-network,” Medical Image Computing and Computer Assisted Intervention (MICCAI): Deep Learning in Medical Image Analysis (DLMIA), Granada, Spain, 2018. (paper) [NVIDIA Best Paper Award]
  • A. Imran, P. Bakic, D. Pokrajac, “Characterization of adipose compartments in mastectomy CT images,” SPIE Medical Imaging: Physics of Medical Imaging, Houston, TX, USA, 2018. (paper)
  • A. Imran, P. Bakic, A. Maidment, D. Pokrajac, “Optimization of the simulation parameters for improving realism in anthropomorphic breast phantoms,” SPIE Medical Imaging: Physics of Medical Imaging, Orlando, FL, USA, 2017. (paper)
  • A. Kuperavage, A. Imran, P. Bakic, A. Maidment, D. Pokrajac, “Validation of Cooper’s ligaments thickness in software phantoms,” SPIE Medical Imaging: Physics of Medical Imaging, Orlando, FL, USA, 2017. (paper)
  • A. Imran, P. Bakic, A. Maidment, D. Pokrajac, “Estimation of adipose compartment volumes in CT images of a mastectomy specimen,” SPIE Medical Imaging: Physics of Medical Imaging, San Diego, CA, USA, 2016. (paper)
  • D. Pokrajac, A. Imran, P. Bakic, “Monte Carlo testing and verifications of numerical algorithm implementations,” International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS), Nis, Serbia, 2015. (paper)

Chapters in Books

  • A. Imran, D. Terzopoulos, “Multi-adversarial variational autoencoder nets for simultaneous image generation and classification,” Deep Learning Applications, Volume 2, 2021. (paper)

(Extended) Abstracts

  • J. Zhang, M. Ahamed, A. Imran, “Deep learning-based metric for CT image quality assessment: Capabilities and potential clinical applications,” Radiological Society of North America (RSNA): Scientific Assembly and Annual Meeting, Chicago, IL, USA, 2024.
  • M. Medrano, A. Imran, S. Wang, A. Wang, “Organ-aware, scout-based approach for scout segmentation and prospective, personalized organ CT dose estimation,” Radiological Society of North America (RSNA): Scientific Assembly and Annual Meeting, Chicago, IL, USA, 2023.
  • H. Jahan, A. Imran, “Pay attention for COVID-19 detection with efficient convolution,” IEEE International Symposium on Biomedical Imaging (ISBI), Kolkata, India, 2022.
  • A. Imran, S. Wang, D. Pal, S. Dutta, B. Patel, E. Zucker, A. Wang, “Real-time, personalized estimation of CT organ dose from scout images,” Radiological Society of North America (RSNA): Scientific Assembly and Annual Meeting, Chicago, IL, USA, 2021.
  • S. Wang, A. Imran, D. Pal, E. Zucker, A. Wang, “Fast Monte Carlo simulation of non-isotropic X-ray source for CT dose calculation,” American Association of Physicists in Medicine (AAPM) Annual Meeting, 2021.
  • A. Imran, C. Huang, H. Tang, W. Fan, Y. Xiao, D. Hao, Z. Qian, D. Terzopoulos, “Self-supervised semi-supervised multi-context learning for the combined classification and segmentation of medical images,” AAAI Conference on Artificial Intelligence, New York, NY, USA, 2020. (paper)
  • A. Imran, C. Huang, H. Tang, W. Fan, K. Cheung, M. To, Z. Qian, D. Terzopoulos, “End-to-End fully automatic segmentation of scoliotic vertebrae from spinal X-ray images,” Medical Imaging Meets NeurIPS (Med-NeurIPS), Vancouver, BC, Canada, 2019. ()
  • A. Imran, C. Huang, H. Tang, W. Fan, K. Cheung, M. To, Z. Qian, D. Terzopoulos, “Bipartite distance for shape-aware landmark detection in spinal X-rays,” Medical Imaging Meets NeurIPS (Med-NeurIPS), Vancouver, BC, Canada, 2019.
  • L. Cockmartin, H. Bosmans, K. Bliznakova, D. Pokrajac, A. Imran, N. Marshall, A. Maidment, P. Bakic, “Creation of realistic structured backgrounds using adipose compartment models in a test object for breast imaging performance analysis,” Radiological Society of North America (RSNA): Scientific Assembly and Annual Meeting, Chicago, IL, USA, 2016.
  • A. Imran, D. Pokrajac, P. Bakic, “Spatial distribution of adipose compartment size, shape, and orientation in CT breast images of a mastectomy specimen,” IEEE Signal Processing in Medicine and Biology (SPMB), Philadelphia, PA, USA, 2015. (paper)

Patents and Patent Applications

  • A. Imran, A. Wang, D. Pal, S. Wang, E. Zucker, B. Patel, “Patient anatomy and task specific automatic exposure control in computed tomography,” U.S. Patent No. 12,002,204, Jun 2024. (Omnibus patent)

Theses

  • A. Imran, “From fully-supervised, single-task to scarcely-supervised, multi-task deep learning for medical image analysis,” Doctoral Dissertation, University of California, Los Angeles, CA, USA, 2020.
  • A. Imran, “Estimation of breast anatomical descriptors from mastectomy CT images,” Masters Thesis, Delaware State University, Dover, DE, USA, 2016.
  • A. Imran, “Automatic extraction of road networks from high-resolution satellite imagery,” Bachelor Thesis, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh, 2012.

Other Selected Publications

  • A. Imran, S. Wang, D. Pal, S. Dutta, B. Patel, E. Zucker, A. Wang, “Scout-Net: Prospective Personalized Estimation of CT Organ Doses from Scout Views,” arXiv preprint arXiv:2312.15354, December 2023, 1–33.
  • M. Ahamed, J. Chen, A. Imran, “Forward-Forward Contrastive Learning,” arXiv:2305.02927, 2023, May 2023, 1–4.
  • A. Purpura-Pontoniere, D. Terzopoulos, A. Wang, A. Imran, “Semi-Supervised Relational Contrastive Learning,” arXiv:2304.05047, April 2023, 1–10.
  • Y. Sarker, A. Imran, M. Ahamed, R. Chakrabortty, M. Ryan, S. Das, “Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder,” arXiv:2204.01711. Apr 2022, 1–10.
  • A. Haque, A. Imran, A. Wang, D. Terzopoulos, “Generalized multi-task learning from substantially unlabeled multi-source medical image data,” arXiv:2110.13185, October 2021, 1–25.
  • A. Haque, A. Wang, A. Imran, “Window-Level is a Strong Denoising Surrogate,” arXiv:2105.07153, May 2021, 1–11.
  • A. Haque, A. Imran, A. Wang, D. Terzopoulos, “MultiMix: Sparingly supervised, extreme multitask learning from medical images,” arXiv:2010.14731, October 2020, 1–5.
  • A. Imran, D. Terzopoulos, “Progressive adversarial semantic segmentation,” arXiv:2005.04311, May 2020, 1–9.
  • A. Imran, C. Huang, H. Tang, W. Fan, K.M.C. Cheung, M. To, Z. Qian, D. Terzopoulos, “Bipartite distance for shape-aware landmark detection in spinal X-ray images,” arXiv:2005.14330, May 2020, 1–3.
  • A. Imran, C. Huang, H. Tang, W. Fan, Y. Xiao, D. Hao, Z. Qian, D. Terzopoulos, “Partly supervised multitask learning,” arXiv:2005.02523, May 2020, 1–10.
  • A. Imran, C. Huang, H. Tang, W. Fan, K.M.C. Cheung, M. To, Z. Qian, D. Terzopoulos, “Analysis of scoliosis from spinal X-ray images,” arXiv:2004.06887, April 2020, 1–6.
  • A. Imran, D. Terzopoulos, “Semi-supervised multi-task learning with chest X-ray images,” arXiv:1908.03693, August 2019, 1–11.
  • A. Imran, D. Terzopoulos, “Multi-adversarial variational autoencoder networks,” arXiv:1906.06430, June 2019, 1–15.
  • A. Imran, A. Hatamizadeh, S.P. Ananth, X. Ding, D. Terzopoulos, N. Tajbakhsh, “Automatic segmentation of pulmonary lobes using a progressive dense V-network,” arXiv:1902.06362, February 2019, 1–8.

Creative Works: Cover Illustrations, Etc.

  • “Lung fissures” color image on the cover of the journal Computer Methods in Biomechanics and Biomedical Engineering (CMBBE): Imaging & Visualization, 9(5–6), December 2020.

Selected Posters

  • A. Moseley, A. Imran, “Context-aware contrastive pretraining for improved medical image segmentation,” Annual Commonwealth Computational Summit (CCS): Artificial Intelligence, Lexington, KY, USA, October 2023.
  • C. Archbold, A. Imran, “Frequency-domain image-to-image translation: A study on breast cancer immunohistochemical imager,” Annual Commonwealth Computational Summit (CCS): Artificial Intelligence, Lexington, KY, USA, October 2023.
  • M. Ahamed, J. Song, J. Zhang, A. Imran, “Deep learning-based optimization of CT acquisition technique for pediatric examinations,” Annual Commonwealth Computational Summit (CCS): Artificial Intelligence, Lexington, KY, USA, October 2023.
  • B. McFarland, M. Ahamed, X. Wang, J. Chen, A. Imran, “Breast cancer lumpectomy specimen margin prediction on radiography,” Markey Cancer Center Research Day Program, Lexington, KY, USA, May 2023.
  • Y. Jiang, S. Gupta, A. Imran, “Transforming radiology workflows: Pretraining for automated chest X-ray report generation,” Medical Imaging with Deep Learning (MIDL), Nashville, TN, USA, July 2023.
  • M. Ahamed, J. Chen, A. Imran, “FFCL: Forward-Forward contrastive learning for improved medical image classification,” Medical Imaging with Deep Learning (MIDL), Nashville, TN, USA, July 2023.