Lung cancer lesion detection in histopathology images using graph-based sparse PCA network.
Lung cancer lesion detection in histopathology images using graph-based sparse PCA network.
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DOI：
10.1016/j.neo.2023.100911
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发表时间：
2023-08
期刊：
影响因子：
4.8
通讯作者：
Galban, Craig J.
中科院分区：
文献类型：
--
作者：
Ram, Sundaresh;Tang, Wenfei;Bell, Alexander J.;Pal, Ravi;Spencer, Cara;Buschhaus, Alexander;Hatt, Charles R.;diMagliano, Marina Pasca;Rehemtulla, Alnawaz;Rodriguez, Jeffrey J.;Galban, Stefanie;Galban, Craig J.
关键词：
肺癌的早期发现对于改善患者生存至关重要。为了满足有效治疗的临床需求，基因工程小鼠模型（GEMM）已成为鉴定和评估这种复杂疾病的分子基础的不可或缺的组成部分，这些分子基础可用作治疗靶点。通过手动检查对组织病理学切片上的GEMM肿瘤负荷进行评估既耗时又容易产生主观偏倚。因此，对于计算机辅助诊断工具，对于这些组织病理学图像的准确和有效的分析，存在需求和挑战的相互作用。在本文中，我们提出了一种简单的机器学习方法，称为基于图的稀疏主成分分析（GS-PCA）网络，用于苏木精和伊红（H&E）染色的组织学肺切片上的癌性病变的自动检测。我们的方法包括四个步骤：1）级联的基于图的稀疏PCA，2）PCA二进制哈希，3）分块直方图，和4）支持向量机（SVM）分类。在我们提出的架构中，基于图的稀疏PCA被用来学习卷积网络的多个阶段的滤波器组。接下来是PCA哈希和块直方图，用于索引和池化。然后将从该GS-PCA中提取的有意义的特征馈送到SVM分类器。我们评估所提出的算法的性能H&E幻灯片从诱导型K-ras肺癌小鼠模型使用精度/召回率，分数，谷本系数，和曲线下面积（AUC）的受试者操作特征（ROC），并表明我们的算法是有效的，并提供了改进的检测精度相比，现有的算法。
Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-ras lung cancer mouse model using precision/recall rates, -score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.
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