Contrastive learning-based Adenoid Hypertrophy Grading Network Using Nasoendoscopic Image

Siting Zheng, Xuechen Li, Mingmin Bi, Yuxuan Wang, Haiyan Liu, Xiaoshan Feng, Yunping Fan, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 35th International Symposium on Computer-Based Medical Systems, CBMS 2022
EditorsLinlin Shen, Alejandro Rodriguez Gonzalez, KC Santosh, Zhihui Lai, Rosa Sicilia, Joao Rafael Almeida, Bridget Kane
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665467704
Publication statusPublished - 2022
Externally publishedYes
Event35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022 - Shenzhen, China
Duration: 21 Jul 202223 Jul 2022

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
ISSN (Print)1063-7125


Conference35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022


  • adenoid hypertrophy
  • contrastive learning
  • convolutional neural network
  • deep learning
  • image classification

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications


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