论文标题
宫颈癌成像中生成对抗网络的最新趋势和分析
Recent trends and analysis of Generative Adversarial Networks in Cervical Cancer Imaging
论文作者
论文摘要
宫颈癌是女性最常见的癌症类型之一。它占女性所有癌症的6-29％。它是由人类乳头状瘤病毒（HPV）引起的。宫颈癌的5年生存机会范围从17％-92％的范围内，具体取决于检测到的阶段。早期发现该疾病有助于改善患者的治疗和存活率。如今，许多深度学习算法被用于检测宫颈癌。一种被称为生成对抗网络（GAN）的深度学习技术的特殊类别正在逐步筛查，检测和分类宫颈癌。在这项工作中，我们介绍了与使用各种GAN模型，其应用以及用于其在宫颈癌成像领域的性能评估的评估指标有关的最新趋势的详细分析。
Cervical cancer is one of the most common types of cancer found in females. It contributes to 6-29% of all cancers in women. It is caused by the Human Papilloma Virus (HPV). The 5-year survival chances of cervical cancer range from 17%-92% depending upon the stage at which it is detected. Early detection of this disease helps in better treatment and survival rate of the patient. Many deep learning algorithms are being used for the detection of cervical cancer these days. A special category of deep learning techniques known as Generative Adversarial Networks (GANs) are catching up with speed in the screening, detection, and classification of cervical cancer. In this work, we present a detailed analysis of the recent trends relating to the use of various GAN models, their applications, and the evaluation metrics used for their performance evaluation in the field of cervical cancer imaging.
