The overall purpose of the ML4MI initiative is to foster interdisciplinary collaboration between machine learning (ML) experts and medical imaging researchers at the University of Wisconsin, in order to develop and apply state-of-the-art ML solutions to challenging problems in medical imaging. This initiative responds to rapidly growing interest in ML techniques within medical imaging research, due to the unprecedented potential to solve challenging problems in areas such as image reconstruction, image processing, and computer-aided diagnosis. ML4MI is generously supported by the UW Departments of Radiology and Medical Physics, and the Grainger Institute for Engineering.
Monthly seminars
A regular seminar series began in February 2018, and includes 1) seminars describing technical developments in ML with potential biomedical applications, 2) seminars by local or external Radiology researchers, describing problems that may benefit from ML approaches and ongoing projects involving ML techniques, and 3) seminars by biomedical researchers (not in Radiology), describing pioneering experiences applying ML in their fields of study. The seminar location will alternate between ECB/WID and SMPH/WIMR. These seminars will also provide an opportunity for UW researchers to become familiar with researchers “on the other side of campus.”
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2021 Seminars
Month | Speaker | Date/Time | Location | Title |
---|---|---|---|---|
Mar | Shengli (Bruce) Jiang, PhD (UW Chemical and Biological Engineering) | 3/3, 2:00pm | Webinar | Blending Expert and Machine Learning Using Quantitative Chest CT and Clinical Biomarkers to Predict Asthma Severity and Outcomes |
Mar | Tim Leiner, MD, PhD, Tenured Professor of Radiology, Chair of Cardiovascular Imaging, Utrecht University Medical Center, Utrecht, The Netherlands |
3/31, 12:00pm | Webinar | Bringing Machine Learning to the Clinic: Challenges and Opportunities |
Apr | Corey W. Arnold, PhD, Associate Professor Depts. of Radiology, Pathology, Electrical & Computer Engineering, Bioengineering, and Bioinformatics, Director, Computational Diagnostics Lab David Geffen School of Medicine, UCLA, Los Angeles, CA | 4/2, 12:00pm | Webinar | Computational Disease Phenotyping Using Machine Learning |
Apr | Shuo Li, PhD, Director, the Digital Imaging Group of London, Associate Professor, the University of Western Ontario and Scientist, Lawson Health Research Institute, London, Ontario, Canada | 4/7, 12:00pm | Webinar | Unlocking the Full Potential of Medical Imaging by Innovative Machine Learning |
May | Meghan Lubner, MD (UW Dept of Radiology) & Dane Morgan, PhD (UW Materials Science and Engineering) | 5/24, 2:00pm | Webinar | Radiomics and Machine learning in identification of aggressive tumor features in Renal Cell Carcinoma (RCC) |
2020 Seminars
Month | Speaker | Date/Time | Location | Title |
---|---|---|---|---|
Jan |
Jong Chul Ye, PhD (KAIST Bio and Brain Engineering) |
1/13, 4:00pm | HSLC 1345 |
Understanding Geometry of Deep Learning for Medical Image Reconstruction |
July | Raúl San José Estépar, PhD (Harvard Radiology) | 7/8, 2:00pm | Web conference | |
Sept | Kevin Johnson, PhD (UW Med Physics) | 9/9, 2:00pm | Web conference |
Fusion of computational fluid dynamic flow data into 4D flow MRI using machine learning |
2019 Seminars
Month | Speaker | Date/Time | Location | Title |
---|---|---|---|---|
Jan 2019 | No Seminar Scheduled | – | – | – |
Feb | James Gee, PhD (U. Penn Radiology) | 2/28, 4pm | SMPH/HSLC Rm 1345 |
Subspecialty-Level Differential Diagnoses by Machine on Clinical Brain MRI |
Mar | Michael Lustig, PhD (Berkeley Electrical Engineering and Computer Science) | 3/28, 1pm | Computer Science (CS) 1240 | Low-Dimensional Models in High-Dimensional MRI |
Apr | Guillermo Sapiro, PhD, MSc (Duke Electrical and Computer Engineering) | 4/24, 4pm |
F2/401 Juhl Conference Room, UW Clinical Science Center |
Computer Vision and Data Science Transforming (Develop)Mental Health and Deep Brain Stimulation |
Oct | Sebastian Raschka, PhD (UW Statistics) | 10/18, 4pm |
1345 HSLC |
Convolutional Neural Networks for Predicting and Hiding Personal Traits from Face Images |
Nov | Maciej Mazurowski, PhD (Duke Radiology) | 11/15, 4pm |
1345 HSLC |
Machine Learning in Radiology |
2018 Seminars
Month | Speaker | Date/Time | Location | Title |
---|---|---|---|---|
Feb 2018 | Rob Nowak, PhD (UW ECE) | 2/15, 4 pm | SMPH/HSLC Rm 1325 | Machine Learning for Medical Imaging |
Mar | Fang Liu, PhD (UW) | 3/23, 4 pm | ME1106 | Deep Learning: Recent Applications in Medical Imaging |
Apr | Tom Grist, MD (UW) | 4/27, 4 pm | ME1106 | Artificial Intelligence in Medical Imaging: Perspective from the International Society for Strategic Studies in Radiology |
May | Joseph Cheng, PhD (Stanford) | 5/24, 4 pm | SMPH/HSLC Rm 1325 | (Re)learning MRI Reconstruction |
June | Amod Jog, PhD (Martinos Center for Biomedical Imaging) | 6/8, 4 pm | SMPH/HSLC Rm 1325 | Pulse Sequence Resilient Fast Brain Segmentation |
Aug | Vikas Singh (UW) | 8/03, 4 pm | SMPH/HSLC Rm 1325 | Visual Relations, Relative Attributes and Graph Neural Networks |
Aug | Curtis Langlotz (Stanford – Radiology) | 8/10, 9-10:30 am | SMPH/HSLC Rm 1325 | Developing a Center of Excellence for Machine Learning Research in Medical Imaging |
Sept | Jayashree Kalpathy-Cramer (Martinos Center for Biomedical Imaging) | 9/27, 4-5pm | SMPH/HSLC Rm 1345 |
Deep Learning in Medical Imaging-Opportunities and Challenges |
Oct | Polina Golland (MIT Electrical Engineering and Computer Science) | 10/25, 4 pm | CS 1240 | Medical Image Imputation |
Nov | Jerry Prince, PhD (Johns Hopkins University, Electrical and Computer Engineering) | 11/15, 4 pm | SMPH/HSLC Rm 1345 | Single Image Super-Resolution for 2D and 3D MRI |
Dec | Juan Santos from HeartVista | 12/14, 4 pm | SMPH/HSLC Rm 1345 |
Pilot grant proposals – 2019 pilot grants awarded
The purpose of this pilot grant program is to foster interdisciplinary collaboration between ML experts and medical imaging clinicians and researchers at the University of Wisconsin’s Departments of Radiology and Medical Physics, and College of Engineering. Specific topics of interest include the development and characterization of novel ML methods with significant medical imaging applications, and the development and validation of new imaging applications for state-of-the-art ML methods. In 2018, pilot grants were awarded to the following collaborative teams: – Kevin Johnson, Alejandro Roldan, and Shiva Rudraraju, “Patient specific hemodynamics using machine learning based fusion of MRI measurements and computational fluid dynamics” – Varun Jog and Alan McMillan, “DeepRad: An accessible, open-source tool for deep learning in medical imaging” In 2019, pilot grants were awarded to the following collaborative teams: – Sean Fain and Victor Zavala, “Blending Expert and Machine Learning Using Quantitative Chest CT and Clinical Biomarkers to Predict Asthma Severity and Outcomes” – Meghan Lubner, Varun Jog, and Dane Morgan, “Application of Machine Learning to CT characterization of Renal Cell Carcinoma“
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RFA Information
This RFA seeks to fund proposals that are likely to spark enduring collaborations and lead to external funding for further research. Pilot awards are $50,000 maximum in direct costs for 12 months of support. Potential applicants are encouraged to contact Diego Hernando (dhernando@wisc.edu) or Varun Jog (vjog@wisc.edu) with questions about programmatic relevance.
DOWNLOAD THE 2019 RFA (now closed)
For any administrative questions, please contact:
- SMPH/Radiology contact: Karen Knipschild (kknipschild@uwhealth.org)
- CoE/GIE contact: Page Metcalf (pmetcalf@wisc.edu)
ML4MI Summer Bootcamps
ML4MI hosts bootcamps with the goal of giving participants a rapid, hands-on introduction into the principles and application of machine learning for medical imaging. This bootcamp is supported by the Grainger Institute of Engineering and the Departments of Radiology and Medical Physics. These bootcamps cover the basics of machine learning and applications for image segmentation, classification, and reconstruction. Please sign up for updates for information on future bootcamps! The source material is also available for self study.
Bootcamp Source Materials:
Bootcamp Organizer Contacts:
- Kevin M. Johnson ( kmjohnson3@wisc.edu)
- Alan McMillan ( AMcmillan@uwhealth.org )
- Tyler Bradshaw ( tbradshaw@wisc.edu )
PREVIOUS WORKSHOP: OCTOBER 5, 2018
This workshop was held on October 5, 2018 at the UW Fluno Center. The one-day workshop featured keynote talks by leaders in the fields of ML and Radiology, a seminar on bioethics, a panel of radiologists, and poster presentations by junior researchers.
- Department of Radiology
- Department of Medical Physics
- Grainger Institute for Engineering
- Machine Learning @ UW Madison
- Center for High Throughput Computing (CHTC)
- Morgridge Institute for Research
- Center for Predictive Computational Phenotyping (CPCP)
- Computation and Informatics in Biology and Medicine (CIBM) training program
- Department of Biostatistics and Biomedical Informatics
- Institute for Clinical and Translational Research
Contact:
- Diego Hernando, PhD (Radiology and Medical Physics, email: dhernando@wisc.edu)
- Varun Jog, PhD (Electrical and Computer Engineering, email: vjog@wisc.edu)
- Kevin Johnson (Medical Physics and Radiology, email: kmjohnson3@wisc.edu)
- Po-Ling Loh, PhD (Electrical and Computer Engineering, email: loh@ece.wisc.edu)
- Alan McMillan (Radiology, email: AMcmillan@uwhealth.org)