A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens.
A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens.
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DOI：
10.3390/s21206708
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发表时间：
2021-10-09
期刊：
影响因子：
--
通讯作者：
El-Baz A
中科院分区：
文献类型：
--
作者：
Hammouda K;Khalifa F;El-Melegy M;Ghazal M;Darwish HE;Abou El-Ghar M;El-Baz A
前列腺癌是美国发病率和死亡率的重要原因。在本文中，我们开发了一个计算机辅助诊断（CAD）系统的自动分级组（GG）分类使用数字化前列腺活检标本（PBS）。我们的CAD系统旨在首先对Gleason模式（GP）进行分类，然后识别Gleason评分（GS）和GG。GP分类管道基于金字塔深度学习系统，该系统利用三个卷积神经网络（CNN）来生成补丁和像素分类。分析从顺序的预处理步骤开始，包括直方图均衡步骤以调整强度值，然后是PBS的边缘增强。然后将数字化的PBS分成三种尺寸的重叠块：100 × 100（），150 × 150（）和200 × 200（）像素，75%重叠。这三种大小的斑块代表了三个金字塔层次。这种金字塔技术使我们能够提取丰富的信息，如较大的补丁提供更多的全球信息，而小补丁提供局部细节。之后，分块技术为每个重叠的分块分配一个标签作为GP类别（1到5）。然后，多数投票是用于获得逐像素分类的核心方法，用于为每个重叠像素获得单个标签。应用这些技术后的结果是三个与原始图像大小相同的图像，每个像素都有一个标签。我们再次对这三张图像使用多数表决技术，只得到一张。建议的框架进行了训练，验证，并测试608全载玻片图像（WSI）的数字化PBS。使用几个指标评估整体诊断准确性：精确度，召回率，F1评分，准确性，宏观平均值和加权平均值。在三种CNN中，（）具有最好的斑块分类精度结果，其分类精度为0.76。发现宏观平均和加权平均度量在0.70-0.77左右。对于GG，我们的CAD结果的准确率约为80%，召回率和F1分数分别在60%至80%之间。此外，准确度和NPV约为94%。为了突出我们的CAD系统的结果，我们使用标准ResNet 50和VGG-16来比较CNN的分块分类结果。同时，我们将GG的结果与以前的工作进行了比较。
Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs’ edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 (), 150 × 150 (), and 200 × 200 (), pixels, and 75% overlap. Those three sizes of patches represent the three pyramidal levels. This pyramidal technique allows us to extract rich information, such as that the larger patches give more global information, while the small patches provide local details. After that, the patch-wise technique assigns each overlapped patch a label as GP categories (1 to 5). Then, the majority voting is the core approach for getting the pixel-wise classification that is used to get a single label for each overlapped pixel. The results after applying those techniques are three images of the same size as the original, and each pixel has a single label. We utilized the majority voting technique again on those three images to obtain only one. The proposed framework is trained, validated, and tested on 608 whole slide images (WSIs) of the digitized PBSs. The overall diagnostic accuracy is evaluated using several metrics: precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged. The () has the best accuracy results for patch classification among the three CNNs, and its classification accuracy is 0.76. The macro-averaged and weighted-average metrics are found to be around 0.70–0.77. For GG, our CAD results are about 80% for precision, and between 60% to 80% for recall and F1-score, respectively. Also, it is around 94% for accuracy and NPV. To highlight our CAD systems’ results, we used the standard ResNet50 and VGG-16 to compare our CNN’s patch-wise classification results. As well, we compared the GG’s results with that of the previous work.
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影响因子：
4.6
作者：
Hammouda, K.;Khalifa, F.;El-Baz, A.
通讯作者：
El-Baz, A.
影响因子：
9.2
作者：
Gislén, A;Dacke, M;Warrant, EJ
通讯作者：
Warrant, EJ
DOI：
10.1056/nejmoa1801993
发表时间：
2018-05-10
期刊：
The New England journal of medicine
影响因子：
--
作者：
通讯作者：
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影响因子：
4.5
作者：
Kvale, Rune;Moller, Bjorn;Halvorsen, Ole J.
通讯作者：
Halvorsen, Ole J.
影响因子：
3.3
作者：
Allsbrook, WC;Mangold, KA;Epstein, JI
通讯作者：
Epstein, JI