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

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.

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.
Pages377-382
Number of pages6
ISBN (Electronic)9781665467704
DOIs
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
Volume2022-July
ISSN (Print)1063-7125

Conference

Conference35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022
Country/TerritoryChina
CityShenzhen
Period21/07/2223/07/22

Keywords

  • 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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