  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.
