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Semi-supervised Graph Learning Framework for Apicomplexan Parasite Classification by Yan Ha; Xiangjie Meng; Zeyu Du; Junfeng Tian; Yu Yuan is a Medicine article available to read on EtoBox.

What is Semi-supervised Graph Learning Framework for Apicomplexan Parasite Classification about?

Apicomplexan parasites cause diseases including malaria, toxoplasmosis, and babesiosis, affecting large parts of the world and hampering economic development to a considerable severity. Early recognition of pathogenic parasites can effectively constrain the spread of these infectious diseases. Recently, deep-learning-based computer-aided parasite recognition systems provide many practical tools for automated apicomplexan parasite recognition. However, existing research works are limited by the complicated and expensive manual annotating workload in supervised deep learning methods, which require many experts’ manually annotated samples. Moreover, they also ignore the significance of correlations among different parasite cells, which retain helpful information for identification. Therefore, this paper is the first to employ a semi-supervised learning strategy to reduce the manual labeling workload and introduce a hybrid graph learning algorithm into parasite recognition. Our approach has a superior parasite recognition capability with semi-supervised techniques and the graph-based model. Specifically, this research proposes a Semi-Supervised Graph Learning (SSGL) framework, composin

Who reads Semi-supervised Graph Learning Framework for Apicomplexan Parasite Classification?

Author

Yan Ha; Xiangjie Meng; Zeyu Du; Junfeng Tian; Yu Yuan

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Physical Sciences)