目的:探讨原发性阴道癌的临床特点,提高临床医师对该病的认识,提醒临床医师尤其对HPV (人乳头瘤病毒)检测阴性原发性阴道癌患者的重视,以便做到早发现、早诊断、早治疗,从而改善患者的预后。方法:本文报道河北省人民医院收治的1例不伴有...目的:探讨原发性阴道癌的临床特点,提高临床医师对该病的认识,提醒临床医师尤其对HPV (人乳头瘤病毒)检测阴性原发性阴道癌患者的重视,以便做到早发现、早诊断、早治疗,从而改善患者的预后。方法:本文报道河北省人民医院收治的1例不伴有HPV感染的原发性阴道鳞状细胞癌患者的诊疗经过并进行探讨,分析其病例特点、影像学检查、病理学检查及诊断,并进行文献复习及总结。结论:原发性阴道癌是一种罕见的妇科肿瘤,临床中原发性阴道癌病例少见,目前缺少大样本、前瞻性的临床研究,基层医院对于本病的诊治经验有限。随着临床医师对原发性阴道癌认识的提高,对该病的治疗方法逐渐个体化。Objective: To explore the clinical characteristics of primary vaginal cancer, enhance clinicians’ awareness of the disease, and emphasize the importance of early detection, diagnosis, and treatment, especially for patients with primary vaginal cancer who test negative for HPV (Human Papillomavirus), thereby improving patients’ prognosis. Methods: This paper reported the diagnosis and treatment of a primary vaginal squamous cell carcinoma without HPV infection admitted to Hebei General Hospital and discussed it, analyzed the case characteristics, imaging examination, pathological examination and diagnosis, and made a literature review and summary. Conclusion: Primary vaginal cancer is a rare gynecological tumor, and the clinical cases of primary vaginal cancer are rare. At present, there is a lack of large samples and prospective clinical studies, and the primary hospitals have limited experience in the diagnosis and treatment of this disease. With the improvement of clinicians’ awareness of primary vaginal cancer, the treatment of the disease is gradually individualized.展开更多
The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on...The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on the YOLOv8 model was proposed in this paper.Firstly,the Global Attention Module(GAM)was introduced to enhance data prediction capability and model expression ability.Secondly,the Space-to-Depth(SPD)module was incorporated into the backbone network for fine-grained feature information learning to mitigate feature information loss due to down-sampling.Finally,a 160 pixels×160 pixels feature layer was added to expand small target feature information and effectively reduce instances of missed targets.Experimental validation on the public VisDrone2019 UAV small target detaset demonstrated that the proposed model achieves significant performance improvement in small target detection tasks compared to existing models,exhibiting higher accuracy.展开更多
文摘目的:探讨原发性阴道癌的临床特点,提高临床医师对该病的认识,提醒临床医师尤其对HPV (人乳头瘤病毒)检测阴性原发性阴道癌患者的重视,以便做到早发现、早诊断、早治疗,从而改善患者的预后。方法:本文报道河北省人民医院收治的1例不伴有HPV感染的原发性阴道鳞状细胞癌患者的诊疗经过并进行探讨,分析其病例特点、影像学检查、病理学检查及诊断,并进行文献复习及总结。结论:原发性阴道癌是一种罕见的妇科肿瘤,临床中原发性阴道癌病例少见,目前缺少大样本、前瞻性的临床研究,基层医院对于本病的诊治经验有限。随着临床医师对原发性阴道癌认识的提高,对该病的治疗方法逐渐个体化。Objective: To explore the clinical characteristics of primary vaginal cancer, enhance clinicians’ awareness of the disease, and emphasize the importance of early detection, diagnosis, and treatment, especially for patients with primary vaginal cancer who test negative for HPV (Human Papillomavirus), thereby improving patients’ prognosis. Methods: This paper reported the diagnosis and treatment of a primary vaginal squamous cell carcinoma without HPV infection admitted to Hebei General Hospital and discussed it, analyzed the case characteristics, imaging examination, pathological examination and diagnosis, and made a literature review and summary. Conclusion: Primary vaginal cancer is a rare gynecological tumor, and the clinical cases of primary vaginal cancer are rare. At present, there is a lack of large samples and prospective clinical studies, and the primary hospitals have limited experience in the diagnosis and treatment of this disease. With the improvement of clinicians’ awareness of primary vaginal cancer, the treatment of the disease is gradually individualized.
文摘The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on the YOLOv8 model was proposed in this paper.Firstly,the Global Attention Module(GAM)was introduced to enhance data prediction capability and model expression ability.Secondly,the Space-to-Depth(SPD)module was incorporated into the backbone network for fine-grained feature information learning to mitigate feature information loss due to down-sampling.Finally,a 160 pixels×160 pixels feature layer was added to expand small target feature information and effectively reduce instances of missed targets.Experimental validation on the public VisDrone2019 UAV small target detaset demonstrated that the proposed model achieves significant performance improvement in small target detection tasks compared to existing models,exhibiting higher accuracy.
