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Machine Learning for Medical Imaging (ML4MI) Initiative Seminar Series

March 23, 2018 @ 4:00 pm - 5:00 pm

From the Department of Radiology:

Please join us at 4PM on Friday, March 23 in Room 1106 Mechanical Engineering, 1513 University Avenue for the Machine Learning for Medical Imaging (ML4MI) Initiative Seminar Series

Professor Diego Hernando has invited Fang Liu, PhD, Assistant Scientist in the Department of Radiology
at UW-Madison.

The Deep Learning: Recent Application in Medical Imaging

This talk will present an overview of Deep Learning (DL) and discuss some recent successful applications in medical imaging.  One aim is to draw connections between DL methods such as convolutional neural network (CNN), convolutional encoder-decoder (CED), cycle-consistent adversarial neural network (Cycle-GAN) and medical applications including image reconstruction, multi-modality image synthesis, image segmentation and computer-assisted image diagnosis.  Dr. Liu will present some of his recent work using DL for medical imaging applications and will discuss relevant DL methods and their strengths and limitations.  The talk will conclude with a discussion of open problems in DL that are particularly relevant in medical imaging and the potential challenges of DL in this emerging field.

For more information on the UW Initiative for Machine Learning in Medical Imaging and upcoming speakers

Bio: Fang Liu’s research interests focus on magnetic resonance (MR) imaging, more specifically for MR image reconstruction, pulse sequence design, quantitative mapping, and MR image analysis. Dr. Liu is currently an assistant scientist in the Department of Radiology, UW-Madison with particular focus on deep learning and medical imaging. Dr. Liu’s projects include three main research fields. Firstly, he leads a research team to investigate advanced methods for performing fast and accurate quantitative mapping techniques for assessing tissue properties in musculoskeletal imaging. These techniques include ultra-short echo time imaging, diffusion imaging, and multi-component relaxometry. Secondly, he designs accelerated MR imaging techniques using deep learning, compressed sensing and MR fingerprinting. Finally, he investigates novel deep learning techniques for medical image applications and recently successfully translated deep learning methods into multiple research projects for automated image segmentation, PET/MR attenuation correction and MR-only radiation therapy.

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March 23, 2018
4:00 pm - 5:00 pm