  Deep learning models have become a popular choice for medical image analysis.
However, the poor generalization performance of deep learning models limits
them from being deployed in the real world as robustness is critical for
medical applications. For instance, the state-of-the-art Convolutional Neural
Networks (CNNs) fail to detect adversarial samples or samples drawn
statistically far away from the training distribution. In this work, we
experimentally evaluate the robustness of a Mahalanobis distance-based
confidence score, a simple yet effective method for detecting abnormal input
samples, in classifying malaria parasitized cells and uninfected cells. Results
indicated that the Mahalanobis confidence score detector exhibits improved
performance and robustness of deep learning models, and achieves
stateof-the-art performance on both out-of-distribution (OOD) and adversarial
samples.
