Abstract
INTRODUCTION: Deep learning-based solutions for histological image classification have gained attention in recent years due to their potential for objective evaluation of histological images. However, these methods often require a large number of expert annotations, which are both time-consuming and labor-intensive to obtain. Several scholars have proposed generative models to augment labeled data, but these often result in label uncertainty due to incomplete learning of the data distribution. METHODS: To alleviate these issues, a method called InceptionV3-SMSG-GAN has been proposed to enhance classification performance by generating high-quality images. Specifically, images synthesized by Multi-Scale Gradients Generative Adversarial Network (MSG-GAN) are selectively added to the training set through a selection mechanism utilizing a trained model to choose generated images with higher class probabilities. The selection mechanism filters the synthetic images that contain ambiguous category information, thus alleviating label uncertainty. RESULTS: Experimental results show that compared with the baseline method which uses InceptionV3, the proposed method can significantly improve the performance of pathological image classification from 86.87% to 89.54% for overall accuracy. Additionally, the quality of generated images is evaluated quantitatively using various commonly used evaluation metrics. DISCUSSION: The proposed InceptionV3-SMSG-GAN method exhibited good classification ability, where histological image could be divided into nine categories. Future work could focus on further refining the image generation and selection processes to optimize classification performance.