Contrastive learning-based Adenoid Hypertrophy Grading Network Using Nasoendoscopic Image

Abstract

Adenoid hypertrophy is a common disease in children with otolaryngology diseases. Otolaryngologists usually use nasoendoscopy for adenoid hypertrophy screening, which is however tedious and time-consuming for the grading. So far, artificial intelligence technology has not been applied to the grading of nasoendoscopic adenoid. In this work, we firstly propose a novel multi-scale grading network, MIB-ANet, for adenoid hypertrophy classification. And we further propose a contrastive learning-based network to alleviate the overfitting problem of the model caused by lacking of nasoendoscopic adenoid images with high-quality annotations. The experimental results show that MIB-ANet shows the best grading performance compared to four classic CNNs, i.e., AlexNet, VGG16, ResNet50 and GoogleNet. Take $F_{1}$ score as an example, MIB-ANet achieves 1.38% higher $F_{1}$ score than the best baseline CNN - AlexNet. Due to the capability of the contrastive learning-based pre-training strategy in exploring unannotated data, the pre-training using SimCLR pretext task can consistently improve the performance of MIB-ANet when different ratios of the labeled training data are employed. The MIB-ANet pre-trained by SimCLR pretext task achieves 4.41%, 2.64%, 3.10%, and 1.71% higher $F_{1}$ score when 25%, 50%, 75% and 100% of the training data are labeled, respectively.