Lab profile
About ci2
The Center for Intelligent Imaging (ci2) focuses on medical imaging by leveraging the power of data, including access to massive image databases, extensive annotated image archives, and domain knowledge at all organ and disease levels. The Center leads in the discovery, innovation and translation of intelligent imaging through clinical radiology, imaging science, engineering, machine learning, and data science.
01 · Research focus
Research areas and highlights
Topics and recent work as stated on the lab’s own site.
- Federated Learning for RadiologyDeveloping a model- and data-agnostic federated learning toolkit to train deep learning algorithms on large, diverse datasets for robust performance across institutions worldwide.
- Improving Detection of Prostate Cancer through Deep Learning
- Applying AI to the Imaging of Chronic Lower Back Pain
- Developing an AI System for Brain MRI Diagnoses
- Machine and Deep Learning Applied to MR Images to Characterize Degenerative Joint Disease
- Automatically Derived Volumetrics for Liver Transplants
- Deep Learning Tools for Early Detection of Alzheimer's Disease
- Integrating Genomics and Imaging to Predict Pain Progression
02 · People
Leadership and researchers
People listed on the lab’s team pages, by role.
03 · Research intelligence
Research Intelligence evidence
Publications, grants and clinical trials linked to the lab’s researchers in Health AI Central’s research data.
Publications & preprints
- Counterfactuals and uncertainty-based explainable paradigm for the automated detection and segmentation of renal cysts in contrast-enhanced abdominal computed tomography: A multi-center study.Artificial intelligence in medicine · 2026
- An Inline-Integrable Image Restoration Model With ROI Fine-Tuning and Slice-to-Volume Reconstruction for Accelerated Whole-Brain Vessel Wall Imaging.Magnetic resonance in medicine · 2026
- End-to-end automated radiotherapy planning using transfer learning to overcome data scarcity.Medical physics · 2026
Active NIH grants
- Development of a Personalized Voxel-wise Prediction of Brain Metastases using Multi-Parametric MR Imaging to Reduce Treatment ToxicityOlivier Morin · NCI · FY 2026
- The development and expansion of automated chest x-ray reading algorithms for childhood tuberculosisDevan Jaganath · NHLBI · FY 2026
- The development and expansion of automated chest x-ray reading algorithms for childhood tuberculosisDevan Jaganath · NHLBI · FY 2026
04 · Updates
Updates and lab news
News and events from the lab’s own site.
- Deep Generative and Hardware Accelerated MRIEventOct 12, 2026 · intelligentimaging.ucsf.edu
- Real-World Federated Learning in MedicineEventSep 14, 2026
- Visiting Graduate Researchers Present Work on Detecting White Matter Abnormalities and Decoding Segmentation Uncertainty in Pediatric Brain TumorsNewsSep 3, 2026
- Visiting Graduate Speaker SeriesEventAug 17, 2026
- A New Framework for Turning Musculoskeletal MRI Into Actionable BiomarkersNewsJul 16, 2026 · intelligentimaging.ucsf.edu
- What Large-Scale Training Data Reveals About the Future of Radiology AINewsJun 26, 2026
- UCSF Residents Present Research on Multilingual Patient-Friendly Radiology Reports and a First-of-Its-Kind CNS Lymphoma DatasetNewsApr 30, 2026
- New Deep Learning Approach Predicts Need for Postnatal Intervention in Fetal VentriculomegalyNewsApr 13, 2026
05 · Official presence
Lab sources and channels
The lab’s own pages and accounts.
06 · Related
Related labs and centers
Labs at the same institution and labs working on similar topics.