Abstract：
Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label-free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.
摘要：
卵巢癌是最常见的妇科癌症之一，也是全球女性癌症相关死亡的第八大原因。手术是癌症治疗的最重要选择之一。手术期间,通常需要活检来筛查病变；然而，传统的病例检查费时费力，需要病理学家丰富的经验和知识。因此,这项研究提出了一个简单的，快,以及结合二次谐波发生（SHG）成像和深度学习的无标记卵巢癌诊断方法。对未染色的新鲜人卵巢组织进行SHG成像并使用金字塔视觉变换器V2(PVTv2)模型准确表征。结果表明，SHG成像的胶原纤维可以量化卵巢癌。此外,PVTv2模型可以准确地将从我们的成像集合中获得的3240张SHG图像区分为良性，正常,和恶性图像，最终准确率为98.4%。这些结果证明了SHG成像技术与深度学习模型相结合用于诊断患病卵巢组织的巨大潜力。
