Abstract
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, Response Score. The key idea is that samples belonging to different classes may have different degrees of influence on a model. We quantify 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.