TABLE 1.

Benefits and limitations of three common deep learning (DL) architectures: U‐Net, GAN (generative adversarial network), and cycle‐GAN

Architecture	Strengths	Limitations
U‐Net	Simplest implementation Stable convergence Fastest training	Paired data only Anatomic misalignments reduce model accuracy and image realism
GAN	Paired or unpaired training Improved image realism due to adversarial loss Model tunability	Moderate implementation difficulty Unstable convergence Slower training Poor structure preservation for unpaired data
Cycle‐GAN	Paired or unpaired training Model tunability Improved image realism due to adversarial loss Good structure preservation	Complex implementation Unstable convergence Slowest training Highest hardware requirements