Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, Dover, DE 19901-2277, USA.
I3MTO Laboratory, University of Orleans, 45067 Orleans, France.
Artif Intell Med. 2020 Jul;107:101885. doi: 10.1016/j.artmed.2020.101885. Epub 2020 Jun 1.
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 clinical imaging patterns into healthy and diseased states. We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of classifiers that we expect to yield more accurate numerical solutions than conventional sparse analyses of the complete spatial domain of the images. We introduce two classification decision strategies based on maximum a posteriori probability (BBMAP), or a log likelihood function (BBLL) and an approach to adjusting the classification decision criteria. To evaluate the performance of the proposed approach we used cross-validation techniques on imaging datasets with disease class labels. We first applied the proposed approach to diagnosis of osteoporosis using bone radiographs. In this problem we assume that changes in trabecular bone connectivity can be captured by intensity patterns. The second application domain is separation of breast lesions into benign and malignant categories in mammograms. The object classes in both of these applications are not linearly separable, and the classification accuracy may depend on the lesion size in the second application. Our results indicate that the proposed integrative sparse analysis addresses the ill-posedness of the approximation problem and produces very good class separation for trabecular bone characterization and for breast lesion characterization. Our approach yields higher classification rates than conventional sparse classification and previously published convolutional neural networks (CNNs) that we fine-tuned for our datasets, or utilized for feature extraction. The BBLL technique also produced higher classification rates than learners using hand-crafted texture features, and the Bag of Keypoints, which is a sophisticated patch-based method. Furthermore, our comparative experiments showed that the BBLL function may yield more accurate classification than BBMAP, because BBLL accounts for possible estimation bias.
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关注。在这项工作中，我们开发了基于稀疏表示的方法，用于将临床成像模式分类为健康和患病状态。我们提出了一种空间块分解方法来解决逼近问题的不规则性，并构建一个分类器集合，我们期望这些分类器比传统的对图像完整空间域的稀疏分析产生更准确的数值解。我们提出了两种分类决策策略，基于最大后验概率（BBMAP）或对数似然函数（BBLL）和一种调整分类决策标准的方法。为了评估所提出方法的性能，我们使用交叉验证技术对具有疾病类别标签的成像数据集进行了评估。我们首先将所提出的方法应用于骨 X 光片的骨质疏松症诊断。在这个问题中，我们假设骨小梁连通性的变化可以通过强度模式来捕获。第二个应用领域是在乳房 X 光片中分离良性和恶性乳房病变。这两个应用中的对象类都不是线性可分的，并且在第二个应用中，分类准确性可能取决于病变的大小。我们的结果表明，所提出的集成稀疏分析解决了逼近问题的不适定性，并为骨小梁特征化和乳房病变特征化产生了非常好的类分离。与我们为数据集微调的传统稀疏分类和之前发布的卷积神经网络（CNN）相比，我们的方法产生了更高的分类率，或者用于特征提取。与使用手工制作的纹理特征的学习者相比，BBLL 技术也产生了更高的分类率，并且与基于补丁的复杂方法 Bag of Keypoints 相比也是如此。此外，我们的比较实验表明，BBLL 函数可能比 BBMAP 产生更准确的分类，因为 BBLL 考虑了可能的估计偏差。
