基于PSVM的主动学习肿块检测方法

肿块区域通常形态各异、差异性较大,并且与正常组织相比没有明显的区别,严重影响了肿块自动检测系统的性能.为了能够有效地提高乳腺X线图像中肿块的检测灵敏度,通过引入包含了样本间相互制约关系的具有成对约束的SVM (PSVM)算法,提出了一种基于PSVM 的主动学习机制.其中,由系统根据样本的不确定性和相互之间的特征匹配距离,主动选择应该反馈给训练集的成对样本.实验结果表明,这种基于PSVM的主动学习方法,能够充分利用样本所包含的信息,使得检测方法具有更好的推广能力和检测性能.

A PSVM-Based Active Learning Method for Mass Detection

In mammograms,masses always vary widely in their shapes and densities,and yet share common appearances with the normal tissues.This point extremely increases the detection difficulty and also impacts the performance of the automatic mass detecting system.To improve the sensitivity of mass detection system,we propose an active learning scheme to detect various masses on mammograms.Firstly,the pairwise constraints are introduced,and the scheme conducts with pairwise support vector machine(PSVM) by involving the relationship among different samples into the classification procedure.Furthermore,according to the detection results,the missed samples with their uncertainty information are combined with the matched feature distance among different samples to provide for re-consideration.Then,with the representative information,the proposed PSVM-based method actively selects the pairwise samples that should be feed back to the training set.The experimental results show that the proposed active learning method with PSVM could make full use of the information of samples,and thus,it could get satisfactory detection rates and false positives during the detection procedure.The method can also get good compromise between the sensitivity and specificity,and the whole learning scheme has better generalization ability and detection performance in comparison with some existing detection methods.