摘要
Abstract
Gynecological tumors refer to malignancies of the female reproductive system,mainly including uterine fibroids,cervical cancer,endometrial cancer,and ovarian cancer.Early diagnosis is crucial for treatment and prognosis.Although ultrasound imaging is the preferred diagnostic modality,it has limitations such as high operator dependence,difficulty in identifying early lesions,and low efficiency in multimodal data analysis.In recent years,artificial intelligence(AI),particularly deep learning(DL),has shown great promise in medical imaging by enabling automatic feature extraction.DL techniques based on ultrasound imaging have been widely applied in gynecological oncology,including tumor detection,classification,preoperative staging,metastasis prediction,and prognosis evaluation.By leveraging methods such as transfer learning,ensemble learning,and multimodal feature fusion,DL algorithms can enhance the diagnostic accuracy of ultrasound in gynecological tumors and facilitate the development of fully automated diagnostic systems.Future research should focus on multi-center studies,integration of multimodal techniques,and the development of lightweight network models to further promote intelligent applications and treatment.关键词
妇科肿瘤/超声检查/深度学习/人工智能/神经网络Key words
gynecological tumors/ultrasound examination/deep learning/artificial intelligence/neural networks
