Jianming Liang

Dr. Liang's research, funded by an NIH R01 grant and multiple state and local grants, is highly interdisciplinary, meshing several branches of computer science and engineering with medical specialties and social sciences and targeting direct advances in human welfare. In addition to his 100+ peer-reviewed publications, he has been awarded 50+ US patents with an additional 70+ patents pending, and his research has led to FDA-approved products.

His research bridges artificial intelligence, medicine, and health, integrating computer vision, machine learning, knowledge graphs, causal modeling, and foundation models to advance medical AI. He advocates that AI foundation models be built upon and grounded in domain fundamentals, anchoring fairness, explainability, and trustworthiness within the sociotechnical systems they serve. His lab develops medical AI foundation models that learn from the foundations of medicine, prioritizing patients’ best interests within real-world healthcare.

Publications: Google Scholar

Publicly Released Software:

1. Training foundation models by accruing and reusing knowledge from heterogeneous expert annotations
   https://github.com/JLiangLab/Ark, 2023 (v1), 2025 (v2)
2. Integrating classification, localization, and segmentation through lock-release pretraining strategy 
   https://github.com/JLiangLab/Foundation_X, 2025
3. Learning anatomically consistent embeddings in composition and decomposition 
   https://github.com/JLiangLab/ACE, 2025
4. Learning anatomy-disease entangled representation
   https://github.com/JLiangLab/LeADER, 2025
5. Learning foundation models from anatomy in medical imaging via self-supervision
   https://github.com/JLiangLab/Eden, 2023 (v1), 2024 (v2)
6. Large-scale benchmarking and boosting transfer learning for medical image analysis
   https://github.com/JLiangLab/BenchmarkTransferLearning, 2021 (v1), 2024 (v2)
7. Learning anatomically consistent embedding for chest radiography
   https://github.com/JLiangLab/PEAC, 2023
8. Achieving discriminative, restorative, and adversarial learning via stepwise incremental pretraining
   https://github.com/JLiangLab/StepwisePretraining, 2022 (v1) and 2023 (v2)
9. Benchmarking and boosting transformers for medical image classification
   https://github.com/JLiangLab/BenchmarkTransformers, 2022
10. Restoring patch order and appearance for self-supervised medical image analysis
    https://github.com/JLiangLab/POPAR, 2022
11. Developing context-aware instance discrimination for a self-supervised framework
    https://github.com/JLiangLab/CAiD, 2022
12. Uniting discriminative, restorative, and adversarial learning for self-supervised medical image analysis
    https://github.com/JLiangLab/DiRA, 2022 (v1) and 2023 (v2)
13. Evaluating and optimizing deep learning methods for computer-aided diagnosis of pulmonary embolism
    https://github.com/jlianglab/CAD_PE, 2021 (v1) and 2023 (v2)
14. Exploiting the semantics of anatomical patterns for self-supervised learning
    https://github.com/JLiangLab/TransVW, 2021
    https://github.com/JLiangLab/SemanticGenesis, 2020
15. Building a primary source of transfer learning for 3D medical imaging applications
    https://github.com/MrGiovanni/ModelsGenesis, 2019 (v1) and 2020 (v2)
16. Learning fixed points in generative adversarial networks
    https://github.com/JLiangLab/Fixed-Point-GAN, 2019
17. Advancing deep architectures for medical image segmentation
    https://github.com/MrGiovanni/UNetplusplus, 2018 (v1) and 2019 (v2)