Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis
This addresses robustness issues in medical image analysis, which is critical for real-world deployment, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of poor generalization and robustness in deep learning models for medical image analysis by evaluating a Mahalanobis distance-based confidence score for detecting abnormal inputs, achieving state-of-the-art performance on both out-of-distribution and adversarial samples in malaria cell classification.
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.