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

TL;DR

This paper introduces a semi-supervised contrastive learning approach for classifying apicomplexan parasites and host cells in microscopic images, effectively reducing the need for labeled data and handling image variability.

Contribution

It proposes a novel semi-supervised contrastive learning method tailored for microscopic image classification with minimal labeled data, addressing fuzzy structures and appearance variations.

Findings

Achieves 94.90% accuracy with only 1% labeled data

Effectively learns appearance-invariant representations

Addresses challenges of fuzzy structures and variable textures

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.