Challenge on Ultrasound Beamforming with Deep Learning (CUBDL) Datasets

Warning message

You must login to view this form.
Submission Due Date:
09/08/2021
Citation Author(s):
Muyinatu
Bell
Johns Hopkins University, USA
Jiaqi
Huang
Johns Hopkins University, USA
Alycen
Wiacek
Johns Hopkins University, USA
Ping
Gong
Mayo Clinic, USA
Shigao
Chen
Mayo Clinic, USA
Ben
Luijten
Eindhoven University of Technology, The Netherlands
Massimo
Mischi
Eindhoven University of Technology, The Netherlands
Xi
Zhang
Tsinghua University, China
Jiawen
Luo
Tsinghua University, China
Vincent
Perrot
Creatis, University of Lyon, INSA, France
Hervé
Liebgott
Creatis, University of Lyon, INSA, France
Ole Marius Hoel
Rindal
University of Oslo, Norway
Alessandro
Ramalli
University of Florence, Italy
Piero
Tortoli
University of Florence, Italy
Olivier
Bernard
Creatis, University of Lyon, INSA, France
Eniola
Oluyemi
Johns Hopkins Medicine, USA
Emily
Ambinder
Johns Hopkins Medicine, USA
Submitted by:
Muyinatu Lediju Bell
Last updated:
Sat, 12/19/2020 - 09:07
DOI:
10.21227/f0hn-8f92
Links:
License:
Creative Commons Attribution

Abstract 

The purpose of this challenge is to provide standardization of methods for assessing and benchmarking deep learning approaches to ultrasound image formation from ultrasound channel data that will live beyond the challenge.

Instructions: 
  • Participants had the freedom to create their own training data to build networks that accomplish specified tasks; this option is still available now that the challenge is closed.
  • Specified tasks and evaluation methods are described on the challenge website: https://cubdl.jhu.edu/
  • Submissions and data sharing is facilitated by IEEE DataPort. 

Although the challenge is now closed, evaluation code remains available (more details on the challenge website https://cubdl.jhu.edu/), and datasets are available for release by submitting a signed user agreement (be sure to include all pages). A journal paper describing challenge submissions, datasets, and the evaluation process implemented by the challenge organizers is forthcoming. 

 

Comments

Are there any test datasets available to get started with?

Yes, please sign up to request access.

As i don't have signed agreement so what can I do to access the data sets

Thanks for your inquiry. The agreement is available for you to sign and submit with your access request.

Im working on US & Deep Learning

Dataset Files

You must be an approved participant in this data competition to access dataset files. To request access you must first login.

Login

Documentation

AttachmentSize
File CUBDL Data Release Agreement.pdf505.78 KB