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.
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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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2018 Seminars
Month | Speaker | Date/Time | Location | Title |
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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
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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 |
2019 Seminars
Month | Speaker | Date/Time | Location | Title | ||
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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 |
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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 |
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ONE-DAY WORKSHOP: OCTOBER 5
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.
Pilot grant proposals – 2018 RFA now closed
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”
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RFA Information
Applications Now Closed
This RFA will seek 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 Po-Ling Loh (loh@ece.wisc.edu) with questions about programmatic relevance.
Important Dates
- Mandatory Letter of Intent Receipt Date: May 15, 2018
- Invitation to submit full application based on selected LOIs: June 1, 2018
- Application Receipt Date: July 15, 2018
- Peer Review Period: July 15-August 7, 2018
- Committee Review Date: August 7-14, 2018
- Award Announcement Date: August 15, 2018
- Grant Start Date: September 1, 2018 – January 1, 2019 (per research team preference)
- Grant End Date: 12 months after grant start date
Download the 2018 RFA
For any administrative questions, please contact:
- SMPH/Radiology contact: Karen Knipschild (kknipschild@uwhealth.org)
- CoE/GIE contact: Page Metcalf (pmetcalf@wisc.edu)
First Annual UW Deep Learning for Medical Imaging Bootcamp
On July 23rd and July 25th, the Department of Radiology hosted Deep Learning Bootcamps as part of the Machine Learning for Medical Imaging initiative. The two bootcamps served nearly 50 interested students, post docs, scientists, and faculty from several UW departments including Radiology, Medical Physics, Electrical and Computer Engineering, Biomedical Engineering, and others to provide hands-on experience in working with deep learning in the context of medical imaging.
The principal organizers and instructors of the event were Tyler Bradshaw, PhD, Jacob Johnson, MS, and Alan McMillan, PhD, from the Department of Radiology and Kevin Johnson, PhD, from Departments of Medical Physics and Radiology.
While the course included discussion of the underpinnings of neural and deep learning networks, the focus was to provide hands-on exercises for participants to gain experience in creating and working with deep learning networks. The hands-on exercises demonstrated the capabilities of deep learning in areas such as detection of disease from chest radiographs, determination of MRI modality, segmentation of lung CT images, conversion of T1-weighted MR images into T2-weighted images, and reconstruction of MR k-space data using a deep learning network. The source code for the exercises has been posted on GitHub.
- 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)
- Po-Ling Loh, PhD (Electrical and Computer Engineering, email: loh@ece.wisc.edu)
- Varun Jog, PhD (Electrical and Computer Engineering, email: vjog@wisc.edu)