PLE-GAN: pseudo-label enhanced generative adversarial networks for medical image classification

Yang Zhou; Hong Shao

doi:10.1117/12.3034922

11 July 2024 PLE-GAN: pseudo-label enhanced generative adversarial networks for medical image classification

Yang Zhou, Hong Shao

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132100H (2024) https://doi.org/10.1117/12.3034922
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Currently, semi-supervised learning methods have become popular in medical image processing tasks, this paper proposes a medical image classification method based on a semi-supervised pseudo-label augmented generative adversarial network. This method combines the generative adversarial network and pseudo-label generation technology, introduces the FID evaluation index to train the generator with good performance for the problem of poor classification performance of GAN, and uses only a small number of real images to generate a sufficiently large number of pseudo-images for model training, which is combined with the pseudo-label generation method of teacher-student network introduced based on MixMatch to categorize the medical images. Through experimental validation, this paper's method outperforms other semi-supervised classification methods in terms of classification accuracy with limited labeling and achieves classification on three publicly available medical image datasets. In addition, we conducted ablation experiments to verify the algorithm's effectiveness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yang Zhou and Hong Shao "PLE-GAN: pseudo-label enhanced generative adversarial networks for medical image classification", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132100H (11 July 2024); https://doi.org/10.1117/12.3034922

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KEYWORDS

Image classification

Medical imaging

Education and training

Data modeling

Gallium nitride

RGB color model

Image enhancement

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