A Semi-supervised Classification Method of Parasites Using Contrastive Learning

Yanni Ren*, Hao Jiang, Huilin Zhu, Yanling Tian, Jinglu Hu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

An expected shortfall of supervised learning for medical imaging classification is the insatiable need for human annotation, which is very expensive. Further, for microscopic parasite images, the salient structures are generally fuzzy, and the insignificant textures are noising. This paper proposes a semi-supervised classification method of three parasites and Erythrocytes microscopic images using a large amount of unlabeled data and a small amount of labeled data for training. It contains a feature extractor trained by contrastive learning and a classifier optimized by Laplacian Support Vector Machine (LapSVM). First, for the deep Convolutional Neural Network feature extractor, we introduce real-world images with similar and clear semantic information to enhance the structure at the representation level. In addition, we introduce variant appearance transformations to eliminate the texture at the representation level. Second, the gated linear network is adopted as the classifier to realize a piecewise linear separation boundary. The parameters are optimized implicitly by LapSVM using a kernel function composed of the representation and the gate control signals, which are generated from the learned feature extractor. The proposed method shows excellent performance when only 1% of the microscopic images are labeled. As a result, we achieve affordable and accurate diagnostic testing in parasite classification.

Original languageEnglish
Pages (from-to)445-453
Number of pages9
JournalIEEJ Transactions on Electrical and Electronic Engineering
Volume17
Issue number3
DOIs
Publication statusPublished - 2022 Mar

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'A Semi-supervised Classification Method of Parasites Using Contrastive Learning'. Together they form a unique fingerprint.

Cite this