会议论文

Liao, BH

英文

2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA

2022

963-968

21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

DEC 12-14, 2022

Nassau, BAHAMAS

[Liao, Binhua;Chen, Yani;Wang, Zhewei;Liu, Jundong] Ohio Univ, Sch Elect Engn & Comp Sci, Athens, OH 45701 USA.^[Liao, Binhua] Huazhong Normal Univ, Coll Math & Stat, Wuhan, Peoples R China.^[Smith, Charles D.] Univ Kentucky, Dept Neurol, Lexington, KY USA.

Wani, MA Kantardzic, M Palade, V Neagu, D Yang, L Chan, KY

10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA

IEEE COMPUTER SOC

978-1-6654-6283-9

10.1109/ICMLA55696.2022.00161

本校为通讯机构

In this paper, we explore the capabilities of a number of deep neural network models in generating whole-brain 3T-like MR images from clinical 1.5T MRIs. The models include a fully convolutional network (FCN) method and three state-of-the-art super-resolution solutions, ESPCN [26], SRGAN [17] and PRSR [7]. The FCN solution, U-Convert-Net, carries out mapping of 1.5T-to-3T slices through a U-Net-like architecture, with 3D neighborhood information integrated through a multi-view ensemble. The pros and cons of the models, as well the associated evaluation metrics, are measured with experiments an...