Rationale

The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of radiological imaging patterns of breast lesions into benign and malignant states.

Methods

We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of classifiers (CL) that we expect to yield more accurate numerical solutions than conventional whole-region of interest (ROI) sparse analyses. We introduce two classification decision strategies based on maximum a posteriori probability (BBMAP-S), or a log likelihood function (BBLL-S).

Results

To evaluate the performance of the proposed approach we used cross-validation techniques on imaging datasets with disease class labels. We utilized the proposed approach for separation of breast lesions into benign and malignant categories in mammograms. The level of difficulty is high in this application and the accuracy may depend on the lesion size. Our results indicate that the proposed integrative sparse analysis addresses the ill-posedness of the approximation problem, producing AUC (area under the receiver operating curve) value of 89.1% for randomized 30-fold cross-validation.

Conclusions

Furthermore, our comparative experiments showed that the BBLL-S decision function may yield more accurate classification than BBMAP-S because BBLL-S accounts for possible estimation bias.

中文翻译：

---

用于乳腺病变表征的空间局部稀疏表示。

 基本原理

在过去的十年中，高维空间中样本的稀疏表示这一主题引起了越来越多的兴趣。在这项工作中，我们开发了基于稀疏表示的方法，用于将乳腺病变的放射成像模式分类为良性和恶性状态。

 方法

我们提出了一种空间块分解方法来解决近似问题的不规则性，并构建一个分类器（CL）集合，我们期望产生比传统的整个感兴趣区域（ROI）稀疏分析更准确的数值解。我们引入了两种基于最大后验概率（BBMAP-S）或对数似然函数（BBLL-S）的分类决策策略。

 结果

为了评估所提出方法的性能，我们对带有疾病类别标签的成像数据集使用了交叉验证技术。我们利用所提出的方法在乳房X光检查中将乳腺病变分为良性和恶性类别。该应用的难度很高，准确性可能取决于病变大小。我们的结果表明，所提出的综合稀疏分析解决了近似问题的不适定性，为随机 30 倍交叉验证产生了 89.1% 的 AUC（受试者工作曲线下面积）值。

 结论

此外，我们的比较实验表明，BBLL-S 决策函数可能比 BBMAP-S 产生更准确的分类，因为 BBLL-S 考虑了可能的估计偏差。