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

翻译：目的：在经尿道膀胱肿瘤切除术（TURBT）过程中，准确的膀胱组织视觉分类对于改善早期癌症诊断和治疗至关重要。TURBT手术中，白光成像（WLI）和窄带成像（NBI）技术被用于病灶检测。每种成像技术提供不同的视觉信息，使临床医生能够识别和分类癌性病变。结合两种成像技术的计算机视觉方法可改善内窥镜诊断。我们解决的问题是：当标注仅存在于一个域（本研究中为WLI域），且内窥镜图像对应非配对数据集（即NBI域和WLI域中不存在一一对应的图像）时，如何进行组织分类。方法：我们提出一种基于半监督生成对抗网络（GAN）的方法，由三个主要组件构成：在标注的WLI数据上训练的教师网络；用于非配对图像到图像转换的循环一致性GAN；以及多输入学生网络。为确保所提GAN生成的合成图像质量，我们与专家合作进行了详细的定量和定性分析。结果：所提方法在组织分类上的总体平均分类准确率、精确率和召回率分别为0.90、0.88和0.89，而在未标注域（NBI）中，相同指标分别为0.92、0.64和0.94。生成图像的质量足以欺骗专家。意义：本研究显示了在多域数据标注有限的情况下，使用基于半监督GAN的膀胱组织分类的潜力。数据集可在https://zenodo.org/record/7741476#.ZBQUK7TMJ6k获取。