Evolutionary Neural Architecture Search for Automatic Esophageal Lesion Identification and Segmentation

Abstract

Automatic esophageal lesion identification (ESEI) is of great importance to clinically aid the endoscopists with the early detection of esophageal cancer. However, accurate identification of esophageal lesion is challenging due to the varying shape, size, illumination condition, and complex background with artifacts in endoscopic images. Although deep neural network based approaches have considerably boosted the performance by automatically learning features from esophageal images, the configuration of the network architecture is highly dependent on domain expertise and is a daunting task to be manually tuned. In this article, we propose an evolutionary algorithm based approach to search for the optimal multitask network architecture for ESEI. Different from existing studies, we first design a multitask network search space, which considers the lesion identification as two steps including esophageal image classification and esophageal lesion segmentation. In particular, the input image resolution is covered in the search space, and the classification utilizes both downsampled and upsampled features. Besides, to avoid scratch training of sampled network architectures in the evolutionary algorithm, the one-shot supernet strategy is developed for searching the optimal network architecture. Results from the performed experiments on a collected sizeable clinical esophageal image dataset show that the proposed method improves on the state of the art on all measured metrics.