In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

The purpose of this paper is to provide an overview of the developments that have occurred
in the Segment Anything Model (SAM) within the medical image segmentation category over …

In the image classification task deep neural networks frequently rely on bias attributes that
are spuriously correlated with a target class in the presence of dataset bias resulting in …

Computer vision systems have witnessed rapid progress over the past two decades due to
multiple advances in the field. As these systems are increasingly being deployed in high …

Biased attributes, spuriously correlated with target labels in a dataset, can problematically
lead to neural networks that learn improper shortcuts for classifications and limit their …

Deep learning models have exhibited tendencies to rely on shortcuts, such as identifying
sheep based solely on the presence of grassland. This reliance often overshadows the true …

Deep neural networks often struggle to learn robust representations in the presence of
dataset biases, leading to suboptimal generalization on unbiased datasets. This limitation …

When trained on biased datasets, Deep Neural Networks (DNNs) often make predictions
based on attributes derived from features spuriously correlated with the target labels. This is …