Infinite Curriculum Learning for Efficiently Detecting Gastric Ulcers in WCE Images
This work addresses the time-consuming and costly inspection of WCE data for gastric ulcer detection, offering a significant efficiency improvement for medical professionals.
The paper tackles the challenge of detecting gastric ulcers in Wireless Capsule Endoscopy (WCE) images by proposing infinite curriculum learning, which adapts the model from local patches to global images, achieving 87% binary classification accuracy and reducing physician workload by 90%-98% in screening.
The Wireless Capsule Endoscopy (WCE) is becoming a popular way of screening gastrointestinal system diseases and cancer. However, the time-consuming process in inspecting WCE data limits its applications and increases the cost of examinations. This paper considers WCE-based gastric ulcer detection, in which the major challenge is to detect the lesions in a local region. We propose an approach named infinite curriculum learning, which generalizes curriculum learning to an infinite sampling space by approximately measuring the difficulty of each patch by its scale. This allows us to adapt our model from local patches to global images gradually, leading to a consistent accuracy gain. Experiments are performed on a large dataset with more than 3 million WCE images. Our approach achieves a binary classification accuracy of 87%, and is able to detect some lesions mis-annotated by the physicians. In a real-world application, our approach can reduce the workload of a physician by 90%-98% in gastric ulcer screening.