Very Similar Appearance Feature Classification for Chronic Endometritis Diagnosis in Hysteroscopy Images

This paper proposes a binary classification framework for chronic endometritis diagnosis in hysteroscopy images. While hysteroscopy is less invasive than a histopathological examination, appearance feature-based diagnosis is not easy due to very similar appearance features of positive and negative samples observed in low-quality images. This work explores various components in our framework to explore the possibility of a less invasive diagnosis as a medical application of machine vision. Our experiments demonstrate that the classification performance of our framework is comparable to manual diagnosis using known symptoms in terms of several metrics.