Grading of invasive breast carcinoma through Grassmannian VLAD encoding.
Grading of invasive breast carcinoma through Grassmannian VLAD encoding.
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DOI：
10.1371/journal.pone.0185110
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Grammalidis N
中科院分区：
文献类型：
--
作者：
Dimitropoulos K;Barmpoutis P;Zioga C;Kamas A;Patsiaoura K;Grammalidis N
在本文中，我们通过编码的组织图像作为VLAD（局部聚集描述子向量）表示在格拉斯曼流形的浸润性乳腺癌的自动分级的问题。该方法将每个图像视为一组多维空间演化信号，可以通过高阶线性动力系统分析有效地建模。随后，每个H&E（苏木精和伊红）染色的乳腺癌组织学图像被表示为格拉斯曼流形上的点云，而基于流形上的局部准则，应用向量表示方法来聚集格拉斯曼点。为了评估所提出方法的效率，使用了两个具有不同特征的数据集。更具体地说，我们创建了一个新的中型数据集，由300张带注释的图像（来自21名患者）组成，分为1级、2级和3级，同时我们还使用一个大型数据集BreaKHis提供了实验结果，该数据集包含来自82名患者的7,909张乳腺癌组织学图像，包括良性和恶性病例。实验结果表明，所提出的方法优于许多最先进的方法，我们的数据集和BreaKHis数据集的平均分类率分别为95.8%和91.38%。
In this paper we address the problem of automated grading of invasive breast carcinoma through the encoding of histological images as VLAD (Vector of Locally Aggregated Descriptors) representations on the Grassmann manifold. The proposed method considers each image as a set of multidimensional spatially-evolving signals that can be efficiently modeled through a higher-order linear dynamical systems analysis. Subsequently, each H&E (Hematoxylin and Eosin) stained breast cancer histological image is represented as a cloud of points on the Grassmann manifold, while a vector representation approach is applied aiming to aggregate the Grassmannian points based on a locality criterion on the manifold. To evaluate the efficiency of the proposed methodology, two datasets with different characteristics were used. More specifically, we created a new medium-sized dataset consisting of 300 annotated images (collected from 21 patients) of grades 1, 2 and 3, while we also provide experimental results using a large dataset, namely BreaKHis, containing 7,909 breast cancer histological images, collected from 82 patients, of both benign and malignant cases. Experimental results have shown that the proposed method outperforms a number of state of the art approaches providing average classification rates of 95.8% and 91.38% with our dataset and the BreaKHis dataset, respectively.
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影响因子：
10.6
作者：
Guo, Zhenhua;Zhang, Lei;Zhang, David
通讯作者：
Zhang, David
DOI：
10.1109/tcsvt.2014.2339592
发表时间：
2015-02-01
影响因子：
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作者：
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通讯作者：
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DOI：
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发表时间：
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期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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