# Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation

## TL;DR

This paper introduces an efficient neural architecture search method for medical image segmentation, using low-dimensional data to reduce search time significantly while maintaining high performance on high-dimensional OCT images.

## Contribution

It proposes a novel NAS approach that searches on low-dimensional data and transfers architectures to high-dimensional data, saving time in medical imaging tasks.

## Key findings

- Search on 1D data reduces search time by 87.5%.
- Final models on 2D data achieve similar performance to those searched directly on 2D.
- Method is effective for OCT layer segmentation.

## Abstract

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.