Deep learning based medical image recognition systems often require a substantial amount of training data with expert annotations, which can be expensive and time-consuming to obtain. Recently, synthetic augmentation techniques have been proposed to mitigate the issue by generating realistic images conditioned on class labels. However, the effectiveness of these methods heavily depends on the representation capability of the trained generative model, which cannot be guaranteed without sufficient labeled training data. To further reduce the dependency on annotated data, we propose a synthetic augmentation method called HistoDiffusion, which can be pre-trained on large-scale unlabeled datasets and later applied to a small-scale labeled dataset for augmented training. In particular, we train a latent diffusion model (LDM) on diverse unlabeled datasets to learn common features and generate realistic images without conditional inputs. Then, we fine-tune the model with classifier guidance in latent space on an unseen labeled dataset so that the model can synthesize images of specific categories. Additionally, we adopt a selective mechanism to only add synthetic samples with high confidence of matching to target labels. We evaluate our proposed method by pre-training on three histopathology datasets and testing on a histopathology dataset of colorectal cancer (CRC) excluded from the pre-training datasets. With HistoDiffusion augmentation, the classification accuracy of a backbone classifier is remarkably improved by 6.4% using a small set of the original labels. Our code is available at https://github.com/karenyyy/HistoDiffAug.

翻译：基于深度学习的医学图像识别系统通常需要大量带有专家标注的训练数据，而获取这些标注数据既昂贵又耗时。近年来，研究者提出了合成增强技术，通过生成与类别标签匹配的真实图像来缓解这一问题。然而，这类方法的有效性高度依赖于训练生成模型的表示能力，而在缺乏足够标注训练数据的情况下，这种能力无法得到保证。为进一步减少对标注数据的依赖，我们提出了一种名为HistoDiffusion的合成增强方法，该方法可在大规模无标签数据集上进行预训练，随后应用于小规模标注数据集以进行增强训练。具体而言，我们在多样化的无标注数据集上训练潜在扩散模型（LDM），以学习通用特征并在无条件输入下生成真实图像。随后，我们在一个未见过的标注数据集上，通过潜在空间中的分类器引导对模型进行微调，使其能够合成特定类别的图像。此外，我们引入了一种选择性机制，仅添加与目标标签高置信度匹配的合成样本。通过在三个组织病理学数据集上进行预训练，并在一个排除在预训练数据集之外的大肠癌（CRC）组织病理学数据集上进行测试，我们评估了所提方法。采用HistoDiffusion增强后，基于少量原始标签，骨干分类器的分类准确率显著提升了6.4%。我们的代码可在https://github.com/karenyyy/HistoDiffAug获取。