In this project, our goal is to develop a method for interpreting how a neural network makes layer-by-layer embedded decisions when trained for a classification task, and also to use this insight for improving the model performance. To do this, we first approximate the distribution of the image representations in these embeddings using random forest models, the output of which, termed embedding outputs, are used for measuring how the network classifies each sample. Next, we design a pipeline to use this layer embedding output to calibrate the original model output for improved probability calibration and classification. We apply this two-steps method in a fully convolutional neural network trained for a liver tissue classification task on our institutional dataset that contains 20 3D multi-parameter MR images for patients with hepatocellular carcinoma, as well as on a public dataset with 131 3D CT images. The results show that our method is not only able to provide visualizations that are easy to interpret, but that the embedded decision-based information is also useful for improving model performance in terms of probability calibration and classification, achieving the best performance compared to other baseline methods. Moreover, this method is computationally efficient, easy to implement, and robust to hyper-parameters.

中文翻译：

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卷积神经网络中的层嵌入分析可改进概率校准和分类。


在这个项目中，我们的目标是开发一种方法来解释神经网络在接受分类任务训练时如何做出逐层嵌入式决策，并利用这种洞察力来提高模型性能。为此，我们首先使用随机森林模型来近似这些嵌入中图像表示的分布，其输出（称为嵌入输出）用于测量网络如何对每个样本进行分类。接下来，我们设计一个管道，使用该层嵌入输出来校准原始模型输出，以改进概率校准和分类。我们将这种两步方法应用到一个完全卷积神经网络中，该网络经过训练，用于在我们的机构数据集（包含肝细胞癌患者的 20 个 3D 多参数 MR 图像）以及包含 131 个 3D CT 的公共数据集上进行肝脏组织分类任务。图像。结果表明，我们的方法不仅能够提供易于解释的可视化效果，而且嵌入的基于决策的信息对于提高模型在概率校准和分类方面的性能也很有用，与其他基线相比，实现了最佳性能方法。此外，该方法计算效率高，易于实现，并且对超参数具有鲁棒性。