目的 总结单纯性大隐静脉曲张的治疗经验。方法 回顾性分析我院2007年3月至2009年11月期间采用改进高位结扎及剥脱术治疗单纯性大隐静脉曲张65例患者的临床资料。结果 本组患者手术时间45～127 min，平均54 min。住院时间5～8 d，平均6.8 d。所有切口均甲级愈合，肿胀不适、沉重感等症状消失，切口皮下无出血、瘀血、血肿，无皮肤麻木等并发症发生。术后随访2～33个月，平均26.9个月，无一例发生深静脉血栓形成，均按期拆线，效果良好，无复发。结论 改进高位结扎剥脱术治疗单纯性大隐静脉曲张疗效确切。
Both feature representation and classifier performance are important factors that determine the performance of computer-aided diagnosis (CAD) systems. In order to improve the performance of ultrasound-based CAD for breast cancers, a novel multiple empirical kernel mapping (MEKM) exclusivity regularized machine (ERM) ensemble classifier algorithm based on self-paced learning (SPL) is proposed, which simultaneously promotes the performance of both feature representation and the classifier. The proposed algorithm first generates multiple groups of features by MEKM to enhance the ability of feature representation, which also work as the kernel transform in multiple support vector machines embedded in ERM. The SPL strategy is then adopted to adaptively select samples from easy to hard so as to gradually train the ERM classifier model with improved performance. This algorithm is verified on a B-mode ultrasound dataset and an elastography ultrasound dataset, respectively. The results show that the classification accuracy, sensitivity and specificity on B-mode ultrasound are (86.36±6.45)%, (88.15±7.12)%, and (84.52±9.38)%, respectively, and the classification accuracy, sensitivity and specificity on elastography ultrasound are (85.97±3.75)%, (85.93±6.09)%, and (86.03±5.88)%, respectively. It indicates that the proposed algorithm can effectively improve the performance of ultrasound-based CAD for breast cancers with the potential for application.