用合成病理图像提升分类性能，选对生成模型是关键。
Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification
- 对比扩散模型、GAN与基于Transformer的生成器，选择适配架构
- 在PCam数据集上，扩散模型适合迁移学习，GAN适合数据增强
- 基于Transformer的模型无需过滤，而CNN类需按真实度筛选
组织病理图像分类对疾病精准诊断至关重要，但依赖大规模多样数据集。然而，因需专家标注和伦理限制，获取真实数据成本高、耗时长。为此，我们研究不同生成模型与图像选择方法，生成基于类别标签的逼真合成病理图像块。实验表明，生成模型类型与架构的选择至关重要：在PCam数据集上，扩散模型适用于迁移学习，而GAN生成样本更适合作为数据增强；相比之下，基于Transformer的生成模型无需图像过滤，而基于卷积神经网络（CNN）的模型则受益于基于真实度评分的筛选。结果表明，合成图像可有效扩充现有数据集，显著提升下游病理图像分类任务的性能。
原文摘要 · Abstract (English)
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.
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