TY  - GEN
N2  - This work introduces novel methods in the field of deformable image registration, both classical and learning-based. A central theme of the work is leveraging Reproducing Kernel Hilbert Space theory as a powerful regularization framework for dense displacement fields that are estimated via image registration.
DO  - 10.6083/ws859g39n
DO  - DOI
AB  - This work introduces novel methods in the field of deformable image registration, both classical and learning-based. A central theme of the work is leveraging Reproducing Kernel Hilbert Space theory as a powerful regularization framework for dense displacement fields that are estimated via image registration.
T1  - Instance and self-supervised learning-based image registration in RKHS
DA  - 2021
AU  - Al Safadi, Ebrahim B.
L1  - https://digitalcollections.ohsu.edu/record/9405/files/AlSafadi.Ebrahim.2021.pdf
PB  - Oregon Health and Science University
PY  - 2021
ID  - 9405
L4  - https://digitalcollections.ohsu.edu/record/9405/files/AlSafadi.Ebrahim.2021.pdf
KW  - Algorithms
KW  - Deep Learning
KW  - Correlation of Data
KW  - machine intelligence
KW  - computer neural network
KW  - medical imaging
TI  - Instance and self-supervised learning-based image registration in RKHS
Y1  - 2021
L2  - https://digitalcollections.ohsu.edu/record/9405/files/AlSafadi.Ebrahim.2021.pdf
LK  - https://digitalcollections.ohsu.edu/record/9405/files/AlSafadi.Ebrahim.2021.pdf
UR  - https://digitalcollections.ohsu.edu/record/9405/files/AlSafadi.Ebrahim.2021.pdf
ER  -