  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

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning