Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification.
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification.
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DOI：
10.1109/cvpr.2016.266
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Saltz JH
中科院分区：
文献类型：
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作者：
Hou L;Samaras D;Kurc TM;Gao Y;Davis JE;Saltz JH
卷积神经网络（CNN）是许多图像分类任务的最先进模型。然而，为了自动识别癌症亚型，在千兆像素分辨率的全载玻片组织图像（WSI）上训练CNN目前在计算上是不可能的。癌症亚型的区分是基于在图像块尺度上观察到的细胞水平视觉特征。因此，我们认为在这种情况下，在图像块上训练块级分类器将比图像级分类器表现得更好或相似。挑战变成了如何智能地组合联合收割机块级分类结果，并对并非所有块都是有区别的这一事实进行建模。我们建议训练一个决策融合模型，以聚合由补丁级CNN给出的补丁级预测，据我们所知，这在以前还没有出现过。此外，我们制定了一个新的期望最大化（EM）的方法，自动定位判别补丁鲁棒利用补丁的空间关系。我们将我们的方法应用于神经胶质瘤和非小细胞肺癌病例的亚型分类。我们的方法的分类精度是类似的病理学家之间的观察者之间的协议。虽然不可能在WSI上训练CNN，但我们通过实验证明，使用较小图像的可比非癌症数据集，基于补丁的CNN可以优于基于图像的CNN。
Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN.