As a training and analysis strategy for convolutional neural networks (CNNs), we slice images into tiled segments and use, for training and prediction, segments that both satisfy an information criterion and contain sufficient content to support classification. In particular, we use image entropy as the information criterion. This ensures that each tile carries as much information diversity as the original image and, for many applications, serves as an indicator of usefulness in classification. To make predictions, a probability aggregation framework is applied to probabilities assigned by the CNN to the input image tiles. This technique, which we call Salient Slices, facilitates the use of large, high-resolution images that would be impractical to analyze unmodified; provides data augmentation for training, which is particularly valuable when image availability is limited; and the ensemble nature of the input for prediction enhances its accuracy.

中文翻译：

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显着切片：使用图像熵改进神经网络训练和性能

作为卷积神经网络 (CNN) 的训练和分析策略，我们将图像切片为平铺段，并使用既满足信息标准又包含足够支持分类的内容的段进行训练和预测。特别是，我们使用图像熵作为信息标准。这确保了每个图块承载的信息多样性与原始图像一样多，并且对于许多应用程序而言，可作为分类有用性的指标。为了进行预测，将概率聚合框架应用于由 CNN 分配给输入图像图块的概率。这种我们称之为显着切片的技术有助于使用大的高分辨率图像，这些图像在未经修改的情况下进行分析是不切实际的；为训练提供数据增强，这在图像可用性有限时特别有价值；预测输入的集成特性提高了其准确性。