Grace Kim, PhD
Professor, UCLA Computer Vision and Imaging Biomarkers (CVIB)
AI and Imaging Biomarkers: Progress and challenges in implementing into patient care
Despite the many papers published, very little AI and quantitative imaging biomarkers are implemented into routine patient care. Technology does not benefit patients unless it is translated and validated in clinical practice, and we believe that the success of AI should be measured in improvements in patient outcomes. Machine learning algorithms must be hardened to handle the variability of real-world imaging and integrated carefully into physician workflows at the point of care. The aim of this workshop is for researchers to exchange experiences, challenges, and progress in translating AI and Imaging Biomarkers into patient care. We look forward to sharing practical CVIB experiences in tackling and overcoming barriers to technology adoption.
- Presenter Registration Due: 1 February 2023
- Attendee Registration Due: 14 February 2023
Workshop Details
- Date: Saturday, Feb 18, 2023 (before SPIE Medical Imaging)
- Meeting Time: 2 pm - 6:30 pm
- Reception: 6:30 pm - 8 pm
- Hosts: UCLA Center for Computer Vision and Imaging Biomarkers (CVIB), Dept of Radiology, David Geffen School of Medicine at UCLA
- Location: UCLA Luskin Conference Center
- Co-Chairs: Grace Kim, John Hoffman, Matthew Brown
Agenda
| 2:00 - 2:15 pm | Welcome & Introduction | Matthew Brown, PhD UCLA CVIB |
| Attendee Presentations (Chair: Grace Kim, PhD) | ||
| 2:15 - 2:35 pm | AAPM task group report 273: Best practices for CAD-AI in medical imaging | Lubomir Hadjiyski Professor in Radiology, University of Michigan |
| 2:35 - 2:55 pm | Detection of small calcifications and their implications for patient management |
Scott Hsieh Assistant Professor, Mayo Clinic Rochester |
| 2:55 - 3:15 pm | Quantitative Analysis of AI-based Digital Twin Technology |
Joon S Park CEO, Medical IP Co, Ltd. |
| 3:15 - 3:30 pm | Poster/Demo Speed Session | |
| 3:30 - 3:45 pm | Coffee Break | |
| Poster/Demo Session: 3:45 - 4:30 pm | ||
| Leveraging challenges faced in autosomal dominant polycystic kidney disease (ADPKD) radiomics of magnetic resonance imaging (MRI) images | Kremer Linnea Medical Physics, University of Chicago |
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| Probabilistic Medical Image imputation via Conditional Adversarial Learning | Ragheb Raad Viterbi School of Engineering, University of Southern California |
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| Assessing Robustness of a Deep Learning Model for COVID-19 Classification on Chest Radiographs | Mena Shenouda Medical Physics, University of Chicago |
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| Image Harmonization in CT for body composition analysis | Jong-Min Kim Leader, Research and Science Division, Medical IP Co, Ltd. |
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| Deep Learning Based Image Segmentation of Prostate Cancer Bone Metastases with nnUNet | Joseph Rich Department of Radiology, Keck School of Medicine at USC |
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| AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections using NCI Imaging Data Commons | Deepa Krishnaswamy Surgical Planning Laboratory, Brigham and Women's Hospital |
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| Catheter Segmentation in Chest X-ray: Improving Imbalanced Segmentation with a Class Frequency Weighted Loss Function | Siyuan Wei CVIB, Physics and Biology in Medicine, David Geffen School of Medicine at UCLA |
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| Reducing dose in renal perfusion CT scans: the effects on quantitative imaging features for patients of different sizes | Morgan Daly CVIB, Physics and Biology in Medicine, David Geffen School of Medicine at UCLA |
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| Using a GAN for CT contrast enhancement to improve CNN kidney segmentation accuracy | Spencer Welland CVIB, Physics and Biology in Medicine, David Geffen School of Medicine at UCLA |
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| Machine Reasoning for Segmentation of the Kidneys on CT images: Improving CNN Performance by Incorporating Anatomical Knowledge in Post-processing | Gabriel Melendez-Corres CVIB, Physics and Biology in Medicine, David Geffen School of Medicine at UCLA |
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| Presentations and Panel Discussion (Chair: John Hoffman, PhD) | ||
| 4:30 - 4:50 pm | The Role of Probabilistic Deep Learning in Medical Imaging | Assad Oberai Hughes Professor and Professor of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California |
| 4:50 - 5:05 pm | AI Reliability and Trustworthiness | M. Wasil Wahi-Anwar, MS UCLA CVIB |
| 5:05 - 5:20 pm | Integration of AI into UCLA Radiology Practice | KP Wong, PhD UCLA CVIB |
| 5:20 - 5:35 pm | Imaging Biomarkers in Pharma Drug Trials | Grace Hyun Kim, PhD UCLA CVIB |
| 5:35 - 5:50 pm | Imaging Biomarkers in Medical Device Trials | Fereidoun Abtin, PhD UCLA CVIB |
| 5:50 - 6:30 pm | Panel Discussion | |
| Reception: 6:30 - 8 pm | ||
Organizing Team
John Hoffman, PhD
Assistant Professor, UCLA Computer Vision and Imaging Biomarkers (CVIB)
Matthew Brown, PhD
Professor and Director, UCLA Center for Computer Vision and Imaging Biomarkers (CVIB)