OF-AR Relation Aware Representation Learning for Lesion Image Segmentation and Grading

Abstract

Medical image segmentation provides important supplementary information for lesion grading, but existing segmentation models usually only focus on the lesion area, which is susceptible to the influence of shooting distance and angle, leading to feature extraction errors. We found an "as one falls, another rises"(OF-AR) relationship between the lesion and the surrounding non lesion areas, and introduced OF-AR relationship aware learning representation to jointly extract features of lesions and non lesions. By dividing the image into black and white dual zones, expanding the feature extraction area, and using a dual zone contrastive learning module to increase the inter-class distance, accurate grading is ensured by utilizing the relative information of two complete targets. In addition, using weighted fusion methods to enhance the comprehensiveness and objectivity of grading. The experiment used adenoids as an example to verify the high accuracy of this method.