Crafting effective deep learning models for medical image analysis is a
complex task, particularly in cases where the medical image dataset lacks
significant inter-class variation. This challenge is further aggravated when
employing such datasets to generate synthetic images using generative
adversarial networks (GANs), as the output of GANs heavily relies on the input
data. In this research, we propose a novel filtering algorithm called Cosine
Similarity-based Image Filtering (CosSIF). We leverage CosSIF to develop two
distinct filtering methods: Filtering Before GAN Training (FBGT) and Filtering
After GAN Training (FAGT). FBGT involves the removal of real images that
exhibit similarities to images of other classes before utilizing them as the
training dataset for a GAN. On the other hand, FAGT focuses on eliminating
synthetic images with less discriminative features compared to real images used
for training the GAN. Experimental results reveal that employing either the
FAGT or FBGT method with modern transformer and convolutional-based networks
leads to substantial performance gains in various evaluation metrics. FAGT
implementation on the ISIC-2016 dataset surpasses the baseline method in terms
of sensitivity by 1.59\% and AUC by 1.88\%. Furthermore, for the HAM10000
dataset, applying FABT outperforms the baseline approach in terms of recall by
13.75\%, and with the sole implementation of FAGT, achieves a maximum accuracy
of 94.44\%.

在医学图像分析中，使用深度学习模型需要克服医学图像数据集中缺乏显著类间变化的复杂任务，而使用生成对抗网络生成合成图像时，该挑战进一步加剧。本研究提出了一种新的过滤算法，名为基于余弦相似度的图像过滤（CosSIF），并分别开发了 FBGT 和 FAGT 两种不同的过滤方法。实验结果表明，使用现代的变压器和卷积网络结合 FAGT 或 FBGT 方法，可以在各种评估指标上实现显著的性能提升。在 ISIC-2016 数据集上，FAGT 方法在敏感性方面超过基线方法 1.59％和 AUC1.88％。此外，在 HAM10000 数据集上，应用 FABT 方法在召回率方面超过基线方法 13.75％，仅使用 FAGT 方法则可以达到 94.44％的最高准确率。