# Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images

arXiv: https://arxiv.org/abs/2301.06366 · HTML: https://mlsanity.com/paper/2301.06366/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 16 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Interpreting the Latent Space of GANs for Semantic Face Editing](https://mlsanity.com/paper/1907.10786/) (2019)
- [Closed-Form Factorization of Latent Semantics in GANs](https://mlsanity.com/paper/2007.06600/) (2020)
- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs](https://mlsanity.com/paper/2010.03975/) (2020)
- [Towards Adversarial Retinal Image Synthesis](https://mlsanity.com/paper/1701.08974/) (2017)
- [This changes to that : Combining causal and non-causal explanations to generate disease progression in capsule endoscopy](https://mlsanity.com/paper/2212.02506/) (2022)
- [PS-DeVCEM: Pathology-sensitive deep learning model for video capsule endoscopy based on weakly labeled data](https://mlsanity.com/paper/2011.12957/) (2020)
- [Learning More for Free - A Multi Task Learning Approach for Improved Pathology Classification in Capsule Endoscopy](https://mlsanity.com/paper/2106.16162/) (2021)
- [From Labels to Priors in Capsule Endoscopy: A Prior Guided Approach for Improving Generalization with Few Labels](https://mlsanity.com/paper/2206.05288/) (2022)
- [Improvement of Multiparametric MR Image Segmentation by Augmenting the Data with Generative Adversarial Networks for Glioma Patients](https://mlsanity.com/paper/1910.00696/) (2019)

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

- [This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation](https://mlsanity.com/paper/2302.02150/) (2023)
- [Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy](https://mlsanity.com/paper/2411.00178/) (2024)
- [Multiscale Vector-Quantized Variational Autoencoder for Endoscopic Image Synthesis](https://mlsanity.com/paper/2511.19578/) (2025)
- [Interpretable Similarity of Synthetic Image Utility](https://mlsanity.com/paper/2512.17080/) (2025)
- [Local Lesion Generation is Effective for Capsule Endoscopy Image Data Augmentation in a Limited Data Setting](https://mlsanity.com/paper/2411.03098/) (2024)
- [Semantic Map Guided Synthesis of Wireless Capsule Endoscopy Images using Diffusion Models](https://mlsanity.com/paper/2311.05889/) (2023)
- [Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition](https://mlsanity.com/paper/2409.13409/) (2024)

## Abstract (arXiv)

Wireless Capsule Endoscopy (WCE) is being increasingly used as an alternative imaging modality for complete and non-invasive screening of the gastrointestinal tract. Although this is advantageous in reducing unnecessary hospital admissions, it also demands that a WCE diagnostic protocol be in place so larger populations can be effectively screened. This calls for training and education protocols attuned specifically to this modality. Like training in other modalities such as traditional endoscopy, CT, MRI, etc., a WCE training protocol would require an atlas comprising of a large corpora of images that show vivid descriptions of pathologies and abnormalities, ideally observed over a period of time. Since such comprehensive atlases are presently lacking in WCE, in this work, we propose a deep learning method for utilizing already available studies across different institutions for the creation of a realistic WCE atlas using StyleGAN. We identify clinically relevant attributes in WCE such that synthetic images can be generated with selected attributes on cue. Beyond this, we also simulate several disease progression scenarios. The generated images are evaluated for realism and plausibility through three subjective online experiments with the participation of eight gastroenterology experts from three geographical locations and a variety of years of experience. The results from the experiments indicate that the images are highly realistic and the disease scenarios plausible. The images comprising the atlas are available publicly for use in training applications as well as supplementing real datasets for deep learning.