Deep Learning for Biomedical Image Reconstruction

The Workshop on Deep Learning for Biomedical Image Reconstruction will be held as part of the 2020 IEEE International Symposium on Biomedical Imaging (ISBI). Machine learning has recently received a large amount of interest for the reconstruction of biomedical and pre-clinical imaging datasets. This workshop focuses on the recent developments and challenges in machine learning for image reconstruction, and its focus is on original work aimed to develop new state-of-the-art techniques and their biomedical imaging applications.

“Deep Learning for Biomedical Image Reconstruction”

Time:  Monday April 6th:  6:00am-10:30am,  Central Standard Time
Organizers:  Mehmet Akcakaya, Ge Wang, Jong Chul Ye

Part 1: Video Streaming and Panel Question Collections

MRI

6:00am-6:30am:  Dr. Mariappan Nadar,   Siemens Healthineers

Scanner Artificial Intelligence: The Road Ahead

6:30am-7:00am:  Dr. Mariya Doneva, Philips Research

Machine Learning in Mri Reconstruction

CT

7:00am-7:30am:  Dr. Bruno De Man, GE Global Research

Neural Networks in Tomographic Imaging: How Much Can They Learn?

7:30am-8:00am:  Dr. Zhou Yu,  Canon Medical Research

How to Serve an AiCE? the Journey of Developing Deep Learning Based Reconstruction Product in Medical Imaging

Theory

8:00am-8:30am:  Prof. Yoram Bresler, University of Illinois at Urbana-Champaign

Deep Learning-Based Image Reconstruction: How the Inverse Problem Informs the Architecture

Clinical Applications

8:30am –9:00am: Prof. Tim Leiner, Utrecht University Medical Center

Bringing Machine Learning to the Clinic – Opportunities and Challenges

Part 2: Live Panel Discussions, and Q&A for Papers

9:00am-10:30am:  Panel discussion and Q&A for authors

CODE: 9gabt
 

Submit Here

The workshop scope covers the machine learning techniques that are used for image acquisition and reconstruction. Specific topics are as follows:

[1] Scope

• Physics-driven neural networks
• Sensor-domain methods
• Neural network optimization and design
• Generative approaches
• Non-neural network approaches
• Denoising, super-resolution and image synthesis methods
• Applications in computed tomography, magnetic resonance imaging, ultrasound imaging and nuclear imaging
• Applications in optical imaging and microscopy
• Multimodal reconstructions or data fusion
• Clinical evaluation
• Challenges for clinical translation

[2] Organizers

1. Mehmet Akcakaya
   Department of Electrical and Computer Engineering
   University of Minnesota
   Minneapolis, Minnesota
2. Ge Wang 
   Department of Biomedical Engineering
   Rensselaer Polytechnic Institute
   Troy, New York
3. Jong Chul Ye
   Department of Bio and Brain Engineering
   Korea Advanced Institute of Science & Technology (KAIST)
   Daejeon 305-701, Korea

[3] Program

• Morning session:  Theory and Algorithms
• Afternoon session: Clinical/Industrial Applications

[4] Presentations

Yoram Bresler
Bruno De Man
Tim Leiner
Mariya Doneva
Zhou Yu
Mariappan Nadar