# Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation

arXiv: https://arxiv.org/abs/2312.03043 · HTML: https://mlsanity.com/paper/2312.03043/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Perception Prioritized Training of Diffusion Models](https://mlsanity.com/paper/2204.00227/) (2022)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80](https://mlsanity.com/paper/2012.12453/) (2020)
- [The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies](https://mlsanity.com/paper/2007.15911/) (2020)
- [Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains](https://mlsanity.com/paper/2210.04133/) (2022)
- [Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation](https://mlsanity.com/paper/1907.02882/) (2019)
- [A Study on the Evaluation of Generative Models](https://mlsanity.com/paper/2206.10935/) (2022)
- [Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models](https://mlsanity.com/paper/2211.00902/) (2022)
- [Improving dermatology classifiers across populations using images generated by large diffusion models](https://mlsanity.com/paper/2211.13352/) (2022)
- [Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets](https://mlsanity.com/paper/2204.05235/) (2022)
- [Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation](https://mlsanity.com/paper/2107.06941/) (2021)

## Built on by (most cited first, 7 of 7)

- [SurGen: Text-Guided Diffusion Model for Surgical Video Generation](https://mlsanity.com/paper/2408.14028/) (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)
- [Surgical Text-to-Image Generation](https://mlsanity.com/paper/2407.09230/) (2024)
- [Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Models](https://mlsanity.com/paper/2505.05573/) (2025)
- [SurGrID: Controllable Surgical Simulation via Scene Graph to Image Diffusion](https://mlsanity.com/paper/2502.07945/) (2025)
- [Mitigating Surgical Data Imbalance with Dual-Prediction Video Diffusion Model](https://mlsanity.com/paper/2510.07345/) (2025)

## Abstract (arXiv)

Recent advances in synthetic imaging open up opportunities for obtaining additional data in the field of surgical imaging. This data can provide reliable supplements supporting surgical applications and decision-making through computer vision. Particularly the field of image-guided surgery, such as laparoscopic and robotic-assisted surgery, benefits strongly from synthetic image datasets and virtual surgical training methods. Our study presents an intuitive approach for generating synthetic laparoscopic images from short text prompts using diffusion-based generative models. We demonstrate the usage of state-of-the-art text-to-image architectures in the context of laparoscopic imaging with regard to the surgical removal of the gallbladder as an example. Results on fidelity and diversity demonstrate that diffusion-based models can acquire knowledge about the style and semantics in the field of image-guided surgery. A validation study with a human assessment survey underlines the realistic nature of our synthetic data, as medical personnel detects actual images in a pool with generated images causing a false-positive rate of 66%. In addition, the investigation of a state-of-the-art machine learning model to recognize surgical actions indicates enhanced results when trained with additional generated images of up to 5.20%. Overall, the achieved image quality contributes to the usage of computer-generated images in surgical applications and enhances its path to maturity.