  Typically, deep learning architectures are handcrafted for their respective
learning problem. As an alternative, neural architecture search (NAS) has been
proposed where the architecture's structure is learned in an additional
optimization step. For the medical imaging domain, this approach is very
promising as there are diverse problems and imaging modalities that require
architecture design. However, NAS is very time-consuming and medical learning
problems often involve high-dimensional data with high computational
requirements. We propose an efficient approach for NAS in the context of
medical, image-based deep learning problems by searching for architectures on
low-dimensional data which are subsequently transferred to high-dimensional
data. For OCT-based layer segmentation, we demonstrate that a search on 1D data
reduces search time by 87.5% compared to a search on 2D data while the final 2D
models achieve similar performance.
