# Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models

arXiv: https://arxiv.org/abs/2308.02587 · HTML: https://mlsanity.com/paper/2308.02587/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 19 of 19)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [PixelSNAIL: An Improved Autoregressive Generative Model](https://mlsanity.com/paper/1712.09763/) (2017)
- [A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images](https://mlsanity.com/paper/2209.13167/) (2022)
- [Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation](https://mlsanity.com/paper/2211.03364/) (2022)
- [Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation](https://mlsanity.com/paper/1907.02882/) (2019)
- [Improving dermatology classifiers across populations using images generated by large diffusion models](https://mlsanity.com/paper/2211.13352/) (2022)
- [Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data](https://mlsanity.com/paper/2103.09276/) (2021)

## Built on by (most cited first, 14 of 14)

- [Deep Generative Models for 3D Medical Image Synthesis](https://mlsanity.com/paper/2410.17664/) (2024)
- [Interactive Generation of Laparoscopic Videos with Diffusion Models](https://mlsanity.com/paper/2406.06537/) (2024)
- [Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion Models](https://mlsanity.com/paper/2410.07753/) (2024)
- [An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis](https://mlsanity.com/paper/2407.03018/) (2024)
- [LCMem: A Universal Model for Robust Image Memorization Detection](https://mlsanity.com/paper/2512.14421/) (2025)
- [SurGrID: Controllable Surgical Simulation via Scene Graph to Image Diffusion](https://mlsanity.com/paper/2502.07945/) (2025)
- [Enabling PSO-Secure Synthetic Data Sharing Using Diversity-Aware Diffusion Models](https://mlsanity.com/paper/2506.17975/) (2025)
- [SeLoRA: Self-Expanding Low-Rank Adaptation of Latent Diffusion Model for Medical Image Synthesis](https://mlsanity.com/paper/2408.07196/) (2024)
- [GAUDA: Generative Adaptive Uncertainty-guided Diffusion-based Augmentation for Surgical Segmentation](https://mlsanity.com/paper/2501.10819/) (2025)
- [Mission Balance: Generating Under-represented Class Samples using Video Diffusion Models](https://mlsanity.com/paper/2505.09858/) (2025)
- [Mitigating Surgical Data Imbalance with Dual-Prediction Video Diffusion Model](https://mlsanity.com/paper/2510.07345/) (2025)
- [LoGex: Improved tail detection of extremely rare histopathology classes via guided diffusion](https://mlsanity.com/paper/2409.01317/) (2024)
- [Anisotropic Diffusion Probabilistic Model for Imbalanced Image Classification](https://mlsanity.com/paper/2409.14313/) (2024)
- [Towards Application Aligned Synthetic Surgical Image Synthesis](https://mlsanity.com/paper/2509.18796/) (2025)

## Abstract (arXiv)

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.