Semi-Supervised Learning of MRI Synthesis Without Fully-Sampled Ground Truths-Reference-Cited by-同舟云学术

Semi-Supervised Learning of MRI Synthesis Without Fully-Sampled Ground Truths

Author:

Yurt Mahmut¹,Dalmaz Onat¹,Dar Salman¹,Ozbey Muzaffer¹^ORCID,Tinaz Berk²,Oguz Kader³,Cukur Tolga¹^ORCID

Affiliation:

1. Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey

2. Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

3. Department of Radiology, Hacettepe University, Ankara, Turkey

Funder

TUBITAK 1001

Turkish Academy of Sciences GEBIP 2015 fellowship

Science Academy BAGEP 2017 fellowship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

Reference68 articles.

5. High-resolution medical image synthesis using progressively grown generative adversarial networks;beers;arXiv 1805 03144,2018

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