Imaging AI Lab

Our Imaging AI lab is dedicated to advancing the use of computer vision and artificial intelligence to interpret and analyze medical images and complex health data. We develop intelligent systems that support clinical decision making and medical discovery.

Our Imaging AI Lab is dedicated to advancing the use of computer vision and artificial intelligence to interpret and analyze medical images and complex health data. Led by Dr. Nick Schaub — an interdisciplinary AI research scientist specializing in large-scale AI applications across biology and medicine — the lab develops intelligent systems that detect subtle pathologies with precision, support clinical decision making, and accelerate medical discovery. In deep academic collaboration with Dr. Craig Jones at the University of Alberta, a pioneering expert in medical imaging AI across MRI, CT, ultrasound, and ophthalmological imaging, the lab bridges cutting-edge LLM research with advanced computer vision to shape a new standard in AI-augmented diagnostic care.

Research Interests

Multi-Modality AI in Healthcare

Unifying Data Streams to Deepen Patient Understanding
This research focuses on integrating diverse data types, such as images, text, and genomics, using AI to generate a more complete picture of patient health. It addresses the challenges of complexity, misalignment, and bias to support safer, more effective medical decision-making.

Longitudinal Prediction

Mapping Health Trajectories Over Time
By analyzing data across the full span of a patient’s healthcare journey, this research enables AI to predict disease progression, treatment response, and long-term outcomes. It helps close gaps in current models that rely heavily on cross-sectional data.

Vascular Atlas

Understanding the Body’s Vascular Blueprint
This work involves creating detailed maps of the body’s vascular system to study blood flow and nutrient delivery. It supports detection of structural changes across diseases like cancer and inflammation, enabling early identification of vascular abnormalities.

  • TYA Liu, Y Liu, MS Gastonguay, D Midgett, N Kuo, Y Zhao, K Ullah, G Alexander, T Hartman, ND Koseoglu, C Jones, “Predicting Imminent Conversion to Exudative Age-related Macular Degeneration Using Multimodal Data and Ensemble Machine Learning”, Ophthalmology Science. 2025. In Press.  doi: https://doi.org/10.1016/j.xops.2025.100785

  • Kim DD, Chandra RS, Yang L, Wu J, Feng X, Atalay M, Bettegowda C, Jones C, Sair H, Liao WH, Zhu C, Zou B, Kazerooni AF, Nabavizadeh A, Jiao Z, Peng J, Bai HX, “Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction”, J Imaging Inform Med. 2024 Oct;37(5):2099-2107. doi: 10.1007/s10278-024-01037-6.

  • Kambli H, Santamaria-Pang A, Tarapov I, Beheshtian E, Luna L, Sair HI, Jones C, “Atlas-BasedLabeling of Resting-State fMRI”, Brain Connectivity, Published Online: 30 May 2024, https://doi.org/10.1089/brain.2023.0080.

  • Liu SZ, Vagdargi P, Jones CK, Luciano M, Anderson WS, Helm PA, Uneri A, Siewerdsen JH, Zbijewski W, and Sisniega A, "One-shot estimation of epistemic uncertainty in deep learning image formation with application to high-quality c one-beam CT reconstruction", SPIE 2024.

  • Liu TYA, Koseoglu ND, Jones CK, “Self-Supervised Deep Learning—The Next Frontier”, JAMA Ophthalmol. Published online February 8, 2024. doi:10.1001/jamaophthalmol.2023.6650.

  • Cornelio A, Martinez AC, Lu H, Jones C, Kashani AH, “Rigid alignment method for secondary analyses of optical coherence tomography volumes”, Biomed. Opt. Express 15, 938-952 (2024) https://doi.org/10.1364/BOE.508123.  

  • Duan R, Caffo B, Bai H, Sair HI, Jones CK. “Evidential Uncertainty Quantification: A Variance Based Perspective.” Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024. 

  • Hu Y, Huang Y, Song A, Jones CK, Siewerdsen JH, Basar B, Helm PA, Uneri A, “Probe positioning for robot-assisted intraoperative ultrasound imaging using deep reinforcement learning”, Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 1292803 (2024) https://doi.org/10.1117/12.3006918.

  • Item description

  • Latheef AAP, Santamaria-Pang A, Jones CK, Sair HI, “Emergent Language Symbolic Autoencoder (ELSA) with Weak Supervision to Model Hierarchical Brain Networks”,  arXiv:2404.10031, MICCAI 2024.

  • Jones CK, Li B, Wu JH, Nakaguchi T, Xuan P, Liu TYA. “Comparative analysis of alignment algorithms for macular optical coherence tomography imaging”, Int J Retin Vitr 2023;9(60) 2023, https://doi.org/10.1186/s40942-023-00497-2.

  • Feng X, Ghimire K, Kim DD, Chandra RS, Zhang H, Peng J, Han B, Huang G, Chen Q, Patel S, Bettegowda C, Sair HI, Jones CK, Jiao Z, Yang L, Bai H. “Brain Tumor Segmentation for Multi-Modal MRI with Missing Information”, J Digit Imaging. 2023 Oct;36(5):2075-2087. doi: 10.1007/s10278-023-00860-7. Epub 2023 Jun 20.

  • Pati S, …. Jones CK, …., Bakas S., “Federated Learning Enables Big Data for Rare Cancer Boundary Detection”, Nature Communications, 2022 Dec 5;13(1):7346. doi: 10.1038/s41467-02233407-5.

  • Liu Y, Ota M, Han R, Siewerdsen J, Liu TYA, Jones CK, “Active Shape Model Registration of Ocular Structures in Computed Tomography Images.” Physics in Medicine and Biology, 2022 (https://doi.org/10.1088/1361-6560/ac9a98).

Federated Learning and Distributed Processing in Healthcare

Collaborative AI That Protects Patient Privacy
This research builds frameworks that allow AI models to be trained on distributed datasets across institutions without sharing sensitive patient data. It enables privacy-preserving, collaborative innovation in medical AI.