A Semi-Supervised Classification Method of Apicomplexan Parasites and Host Cell using Contrastive Learning Strategy

Yanni Ren, Hangyu Deng, Hao Jiang, Huilin Zhu, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A common shortfall of supervised learning for medical imaging is the greedy need for human annotations, which is often expensive and time-consuming to obtain. This paper proposes a semi-supervised classification method for three kinds of apicomplexan parasites and non-infected host cells microscopic images, which uses a small number of labeled data and a large number of unlabeled data for training. There are two challenges in microscopic image recognition. The first is that salient structures of the microscopic images are more fuzzy and intricate than natural images' on a real-world scale. The second is that insignificant textures, like background staining, lightness, and contrast level, vary a lot in samples from different clinical scenarios. To address these challenges, we aim to learn a distinguishable and appearance-invariant representation by contrastive learning strategy. On one hand, macroscopic images, which share similar shape characteristics in morphology, are introduced to contrast for structure enhancement. On the other hand, different appearance transformations, including color distortion and flittering, are utilized to contrast for texture elimination. In the case where only 1% of microscopic images are labeled, the proposed method reaches an accuracy of 94.90% in a generalized testing set.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2973-2978
Number of pages6
ISBN (Electronic)9781665442077
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 - Melbourne, Australia
Duration: 2021 Oct 172021 Oct 20

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Country/TerritoryAustralia
CityMelbourne
Period21/10/1721/10/20

Keywords

  • contrastive learning
  • microscopic image
  • parasite recognition
  • semi-supervised learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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