# Semi-supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images

arXiv: https://arxiv.org/abs/2212.11375 · HTML: https://mlsanity.com/paper/2212.11375/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 71 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [Semi-Supervised Learning with Generative Adversarial Networks](https://mlsanity.com/paper/1606.01583/) (2016)
- [Augmenting Colonoscopy using Extended and Directional CycleGAN for Lossy Image Translation](https://mlsanity.com/paper/2003.12473/) (2020)
- [SLAM Endoscopy enhanced by adversarial depth prediction](https://mlsanity.com/paper/1907.00283/) (2019)
- [Adversarial Self-Defense for Cycle-Consistent GANs](https://mlsanity.com/paper/1908.01517/) (2019)
- [Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation](https://mlsanity.com/paper/2107.06941/) (2021)
- [LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images](https://mlsanity.com/paper/2003.04949/) (2020)
- [Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images](https://mlsanity.com/paper/2003.10033/) (2020)
- [Improving colonoscopy lesion classification using semi-supervised deep learning](https://mlsanity.com/paper/2009.03162/) (2020)
- [Semi-supervised Learning using Adversarial Training with Good and Bad Samples](https://mlsanity.com/paper/1910.08540/) (2019)
- [Joint Learning of Generative Translator and Classifier for Visually Similar Classes](https://mlsanity.com/paper/1912.06994/) (2019)
- [A transfer-learning approach for lesion detection in endoscopic images from the urinary tract](https://mlsanity.com/paper/2104.03927/) (2021)

## Built on by (most cited first, 4 of 4)

- [Improving the Classification Effect of Clinical Images of Diseases for Multi-Source Privacy Protection](https://mlsanity.com/paper/2408.13038/) (2024)
- [Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging](https://mlsanity.com/paper/2412.20070/) (2024)
- [Redefining cystoscopy with ai: bladder cancer diagnosis using an efficient hybrid cnn-transformer model](https://mlsanity.com/paper/2403.03879/) (2024)
- [EndoMatcher: Generalizable Endoscopic Image Matcher via Multi-Domain Pre-training for Robot-Assisted Surgery](https://mlsanity.com/paper/2508.05205/) (2025)

## Abstract (arXiv)

Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based bladder tissue classification when annotations are limited in multi-domain data. The dataset is available at https://zenodo.org/record/7741476#.ZBQUK7TMJ6k