Analysis of the Clever Hans effect in COVID-19 detection using Chest X-Ray images and Bayesian Deep Learning

In recent months, the detection of COVID-19 from radiological images has become a topic of significant interest. Several works have proposed different AI models to demonstrate the feasibility of the application. However, the literature has also reported unwanted behaviours, spurious correlations, and biases of the developed systems that significantly limit their translation to the clinic. This paper deals with a set of interpretability techniques to analyse spurious correlations during the inference, the consistency of the decisions, and the uncertainty of the models, and evaluate the model's performance in a broader and thoughtful way, especially regarding biasing effects, aiming to provide new methodological cues that can increase the systems' robustness. Two different off-the-shelf convolutional neural networks (DenseNet-121 and EfficientNet-B6) were tested along with their Bayesian counterparts. Different saliency maps are used to evaluate the effects of artifacts and confounding factors, and, taking advantage of uncertainty estimations, a new version of the importance of context measure was proposed, to provide more evidence of the spurious correlation affecting models' performance. In view of the results, DenseNet is preferred in both its standard and Bayesian versions, reaching BAcc over 97% training with a large data set (more than 70,000 images). However, results demonstrate that models are significantly affected by the biasing effects, which is minimised by pre-processing with a semantic segmentation of the lungs to guide the learning process towards areas with causal relationships with the problem under study. The conclusions could be extrapolated to the general context of pneumonia detection from chest RX.