Portable edge ai-assisted diagnosis and quality control system for gastrointestinal endoscopy

Abstract

In a decision-support system for gastrointestinal (GI) endoscopy, convolutional neural networks (CNNs) are set up to perform decision-support tasks according to endoscopic images. Each learnable kernel used in the CNNs is advantageously modeled as a linear combination of a set of fixed kernels for simplifying kernel learning, giving a lightweight kernel model to advantageously reduce required computation resources. Further computation-resource reduction can be made by CNN model compression via knowledge distillation and by using multi-task CNNs. It enables the decision-support system to be realized as an edge computing system near a site of performing endoscopic examinations. The system can be automatically configured for esophagogastroduodenoscopy (EGD) or colonoscopy. In the system, lesion-detection results and quality-control results can be seamlessly integrated to provide value-added results, which are more valuable to the endoscopist than separately considering the lesion-detection results and quality-control results.