Deep learning Convolutional Neural Networks have achieved remarkable performance in a variety of classification tasks. The data-driven nature of deep learning indicates that a model behaves in response to the data used to train the model, and the quality of datasets may lead to substantial influence on the model’s performance, especially when dealing with complicated clinical images. In this paper, we propose a simple and novel method to investigate and quantify a deep learning model’s response with respect to a given sample, allowing us to detect out-of-distribution samples based on a newly proposed metric, $\mathit{ResponseScore}$. The key idea is that samples belonging to different classes may have different degrees of influence on a model. Wequantify the resulting consequence of a single sample to a trained-model and relate the quantitative measure of the consequence (by the Response Score) to detect the out-of-distribution samples. The proposed method can find multiple applications such as 1) recognizing abnormal samples, 2) detecting mixed-domain data, and 3) identifying mislabeled data. We present extensive experiments on the three different applications using four biomedical imaging datasets. Experimental results show that our method exhibits remarkable performance and outperforms the compared methods.

深度学习卷积神经网络在各种分类任务中均取得了卓越的性能。深度学习的数据驱动性质表明，模型的行为是对用于训练模型的数据的响应，而数据集的质量可能会对模型的性能产生重大影响，尤其是在处理复杂的临床图像时。在本文中，我们提出了一种简单新颖的方法来调查和量化深度学习模型对给定样本的响应，从而使我们能够根据新提出的指标来检测分布不佳的样本，$\mathit{ResponseScore}$。关键思想是，属于不同类别的样本对模型的影响程度可能不同。我们将单个样本的结果量化为训练模型，并将结果的定量度量（通过响应得分）关联起来，以检测分布不佳的样本。所提出的方法可以找到多种应用，例如1）识别异常样本，2）检测混合域数据以及3）识别标签错误的数据。我们使用四个生物医学成像数据集对三种不同的应用进行了广泛的实验。实验结果表明，该方法具有很好的性能，优于同类方法。