Histopathology image classification is crucial for the accurate identification and diagnosis of various diseases but requires large and diverse datasets. Obtaining such datasets, however, is often costly and time-consuming due to the need for expert annotations and ethical constraints. To address this, we examine the suitability of different generative models and image selection approaches to create realistic synthetic histopathology image patches conditioned on class labels. Our findings highlight the importance of selecting an appropriate generative model type and architecture to enhance performance. Our experiments over the PCam dataset show that diffusion models are effective for transfer learning, while GAN-generated samples are better suited for augmentation. Additionally, transformer-based generative models do not require image filtering, in contrast to those derived from Convolutional Neural Networks (CNNs), which benefit from realism score-based selection. Therefore, we show that synthetic images can effectively augment existing datasets, ultimately improving the performance of the downstream histopathology image classification task.

本研究解决了病理图像分类中对大规模多样化数据集的需求，旨在通过生成模型和图像选择方法生成符合类别标签的真实合成图像补丁。通过对PCam数据集的实验，我们发现扩散模型适用于迁移学习，而GAN生成的样本更适合数据增强，验证了合成图像在提高病理图像分类任务性能中的有效性。

释放合成图像的潜力：一项关于病理图像分类的研究