Cataract surgery is a frequently performed procedure that demands automation and advanced assistance systems. However, gathering and annotating data for training such systems is resource intensive. The publicly available data also comprises severe imbalances inherent to the surgical process. Motivated by this, we analyse cataract surgery video data for the worst-performing phases of a pre-trained downstream tool classifier. The analysis demonstrates that imbalances deteriorate the classifier's performance on underrepresented cases. To address this challenge, we utilise a conditional generative model based on Denoising Diffusion Implicit Models (DDIM) and Classifier-Free Guidance (CFG). Our model can synthesise diverse, high-quality examples based on complex multi-class multi-label conditions, such as surgical phases and combinations of surgical tools. We affirm that the synthesised samples display tools that the classifier recognises. These samples are hard to differentiate from real images, even for clinical experts with more than five years of experience. Further, our synthetically extended data can improve the data sparsity problem for the downstream task of tool classification. The evaluations demonstrate that the model can generate valuable unseen examples, allowing the tool classifier to improve by up to 10% for rare cases. Overall, our approach can facilitate the development of automated assistance systems for cataract surgery by providing a reliable source of realistic synthetic data, which we make available for everyone.

通过对白内障手术视频数据的分析，我们利用一种基于去噪扩散隐式模型（DDIM）和无分类器指导（CFG）的条件生成模型，能够根据复杂的多类别多标签条件（如手术阶段和手术工具组合）合成多样化、高质量的样本，并证实这些合成样本的工具能被分类器识别，在真实图像与合成图像之间难以区分，从而解决了数据稀缺问题，提高了针对罕见情况的工具分类任务的分类器性能。这种方法可以为白内障手术的自动化辅助系统提供可靠的真实合成数据。

使用引导扩散模型合成罕见白内障手术样本