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 classification to improve bladder tissue classification when annotations are limited in multi-domain data.
翻译:目标: 在Bladder Tumor (TURBT) 的跨肠道剖面期间,对膀胱组织进行精确的视觉分类,对于改进早期癌症诊断和治疗至关重要。在TURBT 干预期间,白光成像(WLI)和Narrow Band成像(NBI) 技术用于损伤检测。每种成像技术都提供不同的视觉信息,使临床医生能够识别和分类癌症损伤。使用两种成像技术的计算机视觉方法可以改进内分泌诊断。我们应对组织分类的挑战,因为说明仅在一个领域(我们的情况是WLI,而内分泌图像与非pidaldaldalalalalal 图像对应,也就是说,在NBIBI 和WLI 域中,没有精确的精确度。我们提出半振动的基因回溯网络(GAN) 由三个主要组成部分组成: 师资网络在有标签的WLILI数据上受过训练; 周期性快速的GAN分类, 以进行不精确的图像转换到不精确的图象学的图解, 和精确的数学质量分析是我们所生成的GLILI 。