1 research outputs found
Infinite Curriculum Learning for Efficiently Detecting Gastric Ulcers in WCE Images
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.Comment: 9 pages, 4 figure